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1.

電子ブック
ＥＢ
1. Representation and Management of Narrative Information
Gian Piero Zarri
出版情報: Springer eBooks Computer Science , Springer London, 2009
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目次情報: 続きを見る
Basic Principles / 1：
Narrative Information in an NKRL Context / 1.1：
Narratology and NKRL / 1.1.1：
The Notion of "Event" in an NKRL Context / 1.1.2：
Knowledge Representation and NKRL / 1.2：
"Standard" Ontologies and the "n-ary" Problem / 1.2.1：
A Plain "n-ary" Solution and Some Related Problems / 1.2.2：
In the Guise of Winding Up / 1.3：
The Knowledge Representation Strategy / 2：
Architecture of NKRL: the Four "Components" / 2.1：
The Data Structures of the Four Components / 2.2：
Definitional/Enumerative Data Structures / 2.2.1：
Descriptive/Factual Data Structures / 2.2.2：
Second-order Structures / 2.3：
The Completive Construction / 2.3.1：
Binding Occurrences / 2.3.2：
The Semantic and Ontological Contents / 2.4：
The Organization of the HClass Hierarchy / 3.1：
General Notions about Ontologies / 3.1.1：
HClass Architecture / 3.1.2：
The Organization of the HTemp Hierarchy / 3.2：
Recent Examples of "Structured" Ontological Systems / 3.2.1：
Main Features of Some Specific HTemp Structures / 3.2.2：
The Query and Inference Procedures / 3.3：
"Search Patterns" and Low-level Inferences / 4.1：
The Algorithmic Structure of Fum / 4.1.1：
Temporal Information and Indexing / 4.1.2：
High-level Inference Procedures / 4.2：
General Remarks about Some Reasoning Paradigms / 4.2.1：
Hypothesis Rules / 4.2.2：
Transformation Rules / 4.2.3：
Integrating the Two Main Inferencing Modes of NKRL / 4.2.4：
Inference Rules and Internet Filtering / 4.2.5：
Conclusion / 4.3：
Technological Enhancements / 5.1：
Theoretical Enhancements / 5.2：
Appendix A
Appendix B
References
Index
Basic Principles / 1：
Narrative Information in an NKRL Context / 1.1：
Narratology and NKRL / 1.1.1：
2.

電子ブック
ＥＢ
2. Representation and Management of Narrative Information
Gian Piero Zarri
出版情報: SpringerLink Books - AutoHoldings , Springer London, 2009
所蔵情報: loading…
目次情報: 続きを見る
Basic Principles / 1：
Narrative Information in an NKRL Context / 1.1：
Narratology and NKRL / 1.1.1：
The Notion of "Event" in an NKRL Context / 1.1.2：
Knowledge Representation and NKRL / 1.2：
"Standard" Ontologies and the "n-ary" Problem / 1.2.1：
A Plain "n-ary" Solution and Some Related Problems / 1.2.2：
In the Guise of Winding Up / 1.3：
The Knowledge Representation Strategy / 2：
Architecture of NKRL: the Four "Components" / 2.1：
The Data Structures of the Four Components / 2.2：
Definitional/Enumerative Data Structures / 2.2.1：
Descriptive/Factual Data Structures / 2.2.2：
Second-order Structures / 2.3：
The Completive Construction / 2.3.1：
Binding Occurrences / 2.3.2：
The Semantic and Ontological Contents / 2.4：
The Organization of the HClass Hierarchy / 3.1：
General Notions about Ontologies / 3.1.1：
HClass Architecture / 3.1.2：
The Organization of the HTemp Hierarchy / 3.2：
Recent Examples of "Structured" Ontological Systems / 3.2.1：
Main Features of Some Specific HTemp Structures / 3.2.2：
The Query and Inference Procedures / 3.3：
"Search Patterns" and Low-level Inferences / 4.1：
The Algorithmic Structure of Fum / 4.1.1：
Temporal Information and Indexing / 4.1.2：
High-level Inference Procedures / 4.2：
General Remarks about Some Reasoning Paradigms / 4.2.1：
Hypothesis Rules / 4.2.2：
Transformation Rules / 4.2.3：
Integrating the Two Main Inferencing Modes of NKRL / 4.2.4：
Inference Rules and Internet Filtering / 4.2.5：
Conclusion / 4.3：
Technological Enhancements / 5.1：
Theoretical Enhancements / 5.2：
Appendix A
Appendix B
References
Index
Basic Principles / 1：
Narrative Information in an NKRL Context / 1.1：
Narratology and NKRL / 1.1.1：
3.

電子ブック
ＥＢ
3. Motivated Reinforcement Learning
Kathryn E. Merrick, Mary Lou Maher
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2009
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目次情報: 続きを見る
Non-Player Characters and Reinforcement Learning / Part I：
Non-Player Characters in Multiuser Games / 1：
Types of Multiuser Games / 1.1：
Massively Multiplayer Online Role-Playing Games / 1.1.1：
Multiuser Simulation Games / 1.1.2：
Open-Ended Virtual Worlds / 1.1.3：
Character Roles in Multiuser Games / 1.2：
Existing Artificial Intelligence Techniques for Non-Player Characters in Multiuser Games / 1.3：
Reflexive Agents / 1.3.1：
Learning Agents / 1.3.2：
Evolutionary Agents / 1.3.3：
Smart Terrain / 1.3.4：
Summary / 1.4：
References / 1.5：
Motivation in Natural and Artificial Agents / 2：
Defining Motivation / 2.1：
Biological Theories of Motivation / 2.2：
Drive Theory / 2.2.1：
Motivational State Theory / 2.2.2：
Arousal / 2.2.3：
Cognitive Theories of Motivation / 2.3：
Curiosity / 2.3.1：
Operant Theory / 2.3.2：
Incentive / 2.3.3：
Achievement Motivation / 2.3.4：
Attribution Theory / 2.3.5：
Intrinsic Motivation / 2.3.6：
Social Theories of Motivation / 2.4：
Conformity / 2.4.1：
Cultural Effect / 2.4.2：
Evolution / 2.4.3：
Combined Motivation Theories / 2.5：
Maslow's Hierarchy of Needs / 2.5.1：
Existence Relatedness Growth Theory / 2.5.2：
Towards Motivated Reinforcement Learning / 2.6：
Defining Reinforcement Learning / 3.1：
Dynamic Programming / 3.1.1：
Monte Carlo Methods / 3.1.2：
Temporal Difference Learning / 3.1.3：
Reinforcement Learning in Complex Environments / 3.2：
Partially Observable Environments / 3.2.1：
Function Approximation / 3.2.2：
Hierarchical Reinforcement Learning / 3.2.3：
Motivated Reinforcement Learning / 3.3：
Using a Motivation Signal in Addition to a Reward Signal / 3.3.1：
Using a Motivation Signal Instead of a Reward Signal / 3.3.2：
Comparing the Behaviour of Learning Agents / 3.4：
Player Satisfaction / 4.1：
Psychological Flow / 4.1.1：
Structural Flow / 4.1.2：
Formalising Non-Player Character Behaviour / 4.2：
Models of Optimality for Reinforcement Learning / 4.2.1：
Characteristics of Motivated Reinforcement Learning / 4.2.2：
Comparing Motivated Reinforcement Learning Agents / 4.3：
Statistical Model for Identifying Learned Tasks / 4.3.1：
Behavioural Variety / 4.3.2：
Behavioural Complexity / 4.3.3：
Developing Curious Characters Using Motivated Reinforcement Learning / 4.4：
Curiosity, Motivation and Attention Focus / 5：
Agents in Complex, Dynamic Environments / 5.1：
States / 5.1.1：
Actions / 5.1.2：
Reward and Motivation / 5.1.3：
Motivation and Attention Focus / 5.2：
Observations / 5.2.1：
Events / 5.2.2：
Tasks and Task Selection / 5.2.3：
Experience-Based Reward as Cognitive Motivation / 5.2.4：
Arbitration Functions / 5.2.5：
A General Experience-Based Motivation Function / 5.2.6：
Curiosity as Motivation for Support Characters / 5.3：
Curiosity as Interesting Events / 5.3.1：
Curiosity as Interesting and Competence / 5.3.2：
Motivated Reinforcement Learning Agents / 5.4：
A General Motivated Reinforcement Learning Model / 6.1：
Algorithms for Motivated Reinforcement Learning / 6.2：
Motivated Flat Reinforcement Learning / 6.2.1：
Motivated Multioption Reinforcement Learning / 6.2.2：
Motivated Hierarchical Reinforcement Learning / 6.2.3：
Curious Characters in Games / 6.3：
Curious Characters for Multiuser Games / 7：
Motivated Reinforcement Learning for Support Characters in Massively Multiplayer Online Role-Playing Games / 7.1：
Character Behaviour in Small-Scale, Isolated Games Locations / 7.2：
Case Studies of Individual Characters / 7.2.1：
General Trends in Character Behaviour / 7.2.2：
Curious Characters for Games in Complex, Dynamic Environments / 7.3：
Designing Characters That Can Multitask / 8.1：
Designing Characters for Complex Tasks / 8.1.1：
Games That Change While Characters Are Learning / 8.2.1：
Curious Characters for Games in Second Life / 8.3.1：
Motivated Reinforcement Learning in Open-Ended Simulation Games / 9.1：
Game Design / 9.1.1：
Character Design / 9.1.2：
Evaluating Character Behaviour in Response to Game Play Sequences / 9.2：
Discussion / 9.2.1：
Future / 9.3：
Towards the Future / 10：
Using Motivated Reinforcement Learning in Non-Player Characters / 10.1：
Other Gaming Applications for Motivated Reinforcement Learning / 10.2：
Dynamic Difficulty Adjustment / 10.2.1：
Procedural Content Generation / 10.2.2：
Beyond Curiosity / 10.3：
Biological Models of Motivation / 10.3.1：
Cognitive Models of Motivation / 10.3.2：
Social Models of Motivation / 10.3.3：
Combined Models of Motivation / 10.3.4：
New Models of Motivated Learning / 10.4：
Motivated Supervised Learning / 10.4.1：
Motivated Unsupervised Learning / 10.4.2：
Evaluating the Behaviour of Motivated Learning Agents / 10.5：
Concluding Remarks / 10.6：
Index / 10.7：
Non-Player Characters and Reinforcement Learning / Part I：
Non-Player Characters in Multiuser Games / 1：
Types of Multiuser Games / 1.1：
4.

電子ブック
ＥＢ
4. Motivated Reinforcement Learning
Kathryn E. Merrick, Mary Lou Maher
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2009
所蔵情報: loading…
目次情報: 続きを見る
Non-Player Characters and Reinforcement Learning / Part I：
Non-Player Characters in Multiuser Games / 1：
Types of Multiuser Games / 1.1：
Massively Multiplayer Online Role-Playing Games / 1.1.1：
Multiuser Simulation Games / 1.1.2：
Open-Ended Virtual Worlds / 1.1.3：
Character Roles in Multiuser Games / 1.2：
Existing Artificial Intelligence Techniques for Non-Player Characters in Multiuser Games / 1.3：
Reflexive Agents / 1.3.1：
Learning Agents / 1.3.2：
Evolutionary Agents / 1.3.3：
Smart Terrain / 1.3.4：
Summary / 1.4：
References / 1.5：
Motivation in Natural and Artificial Agents / 2：
Defining Motivation / 2.1：
Biological Theories of Motivation / 2.2：
Drive Theory / 2.2.1：
Motivational State Theory / 2.2.2：
Arousal / 2.2.3：
Cognitive Theories of Motivation / 2.3：
Curiosity / 2.3.1：
Operant Theory / 2.3.2：
Incentive / 2.3.3：
Achievement Motivation / 2.3.4：
Attribution Theory / 2.3.5：
Intrinsic Motivation / 2.3.6：
Social Theories of Motivation / 2.4：
Conformity / 2.4.1：
Cultural Effect / 2.4.2：
Evolution / 2.4.3：
Combined Motivation Theories / 2.5：
Maslow's Hierarchy of Needs / 2.5.1：
Existence Relatedness Growth Theory / 2.5.2：
Towards Motivated Reinforcement Learning / 2.6：
Defining Reinforcement Learning / 3.1：
Dynamic Programming / 3.1.1：
Monte Carlo Methods / 3.1.2：
Temporal Difference Learning / 3.1.3：
Reinforcement Learning in Complex Environments / 3.2：
Partially Observable Environments / 3.2.1：
Function Approximation / 3.2.2：
Hierarchical Reinforcement Learning / 3.2.3：
Motivated Reinforcement Learning / 3.3：
Using a Motivation Signal in Addition to a Reward Signal / 3.3.1：
Using a Motivation Signal Instead of a Reward Signal / 3.3.2：
Comparing the Behaviour of Learning Agents / 3.4：
Player Satisfaction / 4.1：
Psychological Flow / 4.1.1：
Structural Flow / 4.1.2：
Formalising Non-Player Character Behaviour / 4.2：
Models of Optimality for Reinforcement Learning / 4.2.1：
Characteristics of Motivated Reinforcement Learning / 4.2.2：
Comparing Motivated Reinforcement Learning Agents / 4.3：
Statistical Model for Identifying Learned Tasks / 4.3.1：
Behavioural Variety / 4.3.2：
Behavioural Complexity / 4.3.3：
Developing Curious Characters Using Motivated Reinforcement Learning / 4.4：
Curiosity, Motivation and Attention Focus / 5：
Agents in Complex, Dynamic Environments / 5.1：
States / 5.1.1：
Actions / 5.1.2：
Reward and Motivation / 5.1.3：
Motivation and Attention Focus / 5.2：
Observations / 5.2.1：
Events / 5.2.2：
Tasks and Task Selection / 5.2.3：
Experience-Based Reward as Cognitive Motivation / 5.2.4：
Arbitration Functions / 5.2.5：
A General Experience-Based Motivation Function / 5.2.6：
Curiosity as Motivation for Support Characters / 5.3：
Curiosity as Interesting Events / 5.3.1：
Curiosity as Interesting and Competence / 5.3.2：
Motivated Reinforcement Learning Agents / 5.4：
A General Motivated Reinforcement Learning Model / 6.1：
Algorithms for Motivated Reinforcement Learning / 6.2：
Motivated Flat Reinforcement Learning / 6.2.1：
Motivated Multioption Reinforcement Learning / 6.2.2：
Motivated Hierarchical Reinforcement Learning / 6.2.3：
Curious Characters in Games / 6.3：
Curious Characters for Multiuser Games / 7：
Motivated Reinforcement Learning for Support Characters in Massively Multiplayer Online Role-Playing Games / 7.1：
Character Behaviour in Small-Scale, Isolated Games Locations / 7.2：
Case Studies of Individual Characters / 7.2.1：
General Trends in Character Behaviour / 7.2.2：
Curious Characters for Games in Complex, Dynamic Environments / 7.3：
Designing Characters That Can Multitask / 8.1：
Designing Characters for Complex Tasks / 8.1.1：
Games That Change While Characters Are Learning / 8.2.1：
Curious Characters for Games in Second Life / 8.3.1：
Motivated Reinforcement Learning in Open-Ended Simulation Games / 9.1：
Game Design / 9.1.1：
Character Design / 9.1.2：
Evaluating Character Behaviour in Response to Game Play Sequences / 9.2：
Discussion / 9.2.1：
Future / 9.3：
Towards the Future / 10：
Using Motivated Reinforcement Learning in Non-Player Characters / 10.1：
Other Gaming Applications for Motivated Reinforcement Learning / 10.2：
Dynamic Difficulty Adjustment / 10.2.1：
Procedural Content Generation / 10.2.2：
Beyond Curiosity / 10.3：
Biological Models of Motivation / 10.3.1：
Cognitive Models of Motivation / 10.3.2：
Social Models of Motivation / 10.3.3：
Combined Models of Motivation / 10.3.4：
New Models of Motivated Learning / 10.4：
Motivated Supervised Learning / 10.4.1：
Motivated Unsupervised Learning / 10.4.2：
Evaluating the Behaviour of Motivated Learning Agents / 10.5：
Concluding Remarks / 10.6：
Index / 10.7：
Non-Player Characters and Reinforcement Learning / Part I：
Non-Player Characters in Multiuser Games / 1：
Types of Multiuser Games / 1.1：
5.

電子ブック
ＥＢ
5. Automata for Branching and Layered Temporal Structures
Gabriele Puppis, Takeo Kanade
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2010
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Introduction / 1：
Word Automata and Time Granularities / 2：
Background Knowledge / 2.1：
Words and Languages / 2.1.1：
Periodicity of Words / 2.1.2：
Word Automata / 2.1.3：
Time Granularities / 2.1.4：
The String-Based and Automaton-Based Approaches / 2.2：
The Granspec Formalism / 2.2.1：
From Granspecs to Single-String Automata / 2.2.2：
Counters and Multiple Transitions / 2.2.3：
The Logical Counterpart of RCSSA / 2.2.4：
Compact and Tractable Representations / 2.3：
Nested Repetitions of Words / 2.3.1：
Algorithms on NCSSA / 2.3.2：
Optimizing Representations / 2.3.3：
Reasoning on Sets of Granularities / 2.4：
Languages of Ultimately Periodic Words / 2.4.1：
Ultimately Periodic Automata / 2.4.2：
Algorithms on UPA / 2.4.3：
Applications to Time Granularity / 2.4.4：
Discussion / 2.5：
Tree Automata and Logics / 3：
Graphs and Trees / 3.1：
Tree Automata / 3.1.2：
Monadic Second-Order Logic / 3.1.3：
The Model Checking Problem / 3.1.4：
The Contraction Method for Tree Automata / 3.2：
Features and Types / 3.2.1：
Types and the Acceptance Problem / 3.2.2：
From Trees to Their Retractions / 3.2.3：
An Example / 3.2.4：
Tree Transformations / 3.3：
Tree Recolorings / 3.3.1：
Tree Substitutions / 3.3.2：
Tree Transducers / 3.3.3：
Inverse Substitutions / 3.3.4：
A Summary / 3.3.5：
The Class of Reducible Trees / 3.4：
Compositional Properties of Types / 3.4.1：
Closure Properties / 3.4.2：
Effectiveness of the Contraction Method / 3.5：
Reducible Trees and the Caucal Hierarchy / 3.5.1：
Two-Way Alternating Tree Automata / 3.5.2：
Morphic Trees / 3.5.3：
Layered Temporal Structures / 3.5.4：
Summary / 3.6：
Technical Proofs / A：
Proofs of Theorem 5 and Theorem 6 / A.l：
Proof of Theorem 8 / A.2：
Proof of Proposition 34 / A.3：
References
Notation
Index
Introduction / 1：
Word Automata and Time Granularities / 2：
Background Knowledge / 2.1：
6.

電子ブック
ＥＢ
6. Automata for Branching and Layered Temporal Structures
Gabriele Puppis, Takeo Kanade
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2010
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1：
Word Automata and Time Granularities / 2：
Background Knowledge / 2.1：
Words and Languages / 2.1.1：
Periodicity of Words / 2.1.2：
Word Automata / 2.1.3：
Time Granularities / 2.1.4：
The String-Based and Automaton-Based Approaches / 2.2：
The Granspec Formalism / 2.2.1：
From Granspecs to Single-String Automata / 2.2.2：
Counters and Multiple Transitions / 2.2.3：
The Logical Counterpart of RCSSA / 2.2.4：
Compact and Tractable Representations / 2.3：
Nested Repetitions of Words / 2.3.1：
Algorithms on NCSSA / 2.3.2：
Optimizing Representations / 2.3.3：
Reasoning on Sets of Granularities / 2.4：
Languages of Ultimately Periodic Words / 2.4.1：
Ultimately Periodic Automata / 2.4.2：
Algorithms on UPA / 2.4.3：
Applications to Time Granularity / 2.4.4：
Discussion / 2.5：
Tree Automata and Logics / 3：
Graphs and Trees / 3.1：
Tree Automata / 3.1.2：
Monadic Second-Order Logic / 3.1.3：
The Model Checking Problem / 3.1.4：
The Contraction Method for Tree Automata / 3.2：
Features and Types / 3.2.1：
Types and the Acceptance Problem / 3.2.2：
From Trees to Their Retractions / 3.2.3：
An Example / 3.2.4：
Tree Transformations / 3.3：
Tree Recolorings / 3.3.1：
Tree Substitutions / 3.3.2：
Tree Transducers / 3.3.3：
Inverse Substitutions / 3.3.4：
A Summary / 3.3.5：
The Class of Reducible Trees / 3.4：
Compositional Properties of Types / 3.4.1：
Closure Properties / 3.4.2：
Effectiveness of the Contraction Method / 3.5：
Reducible Trees and the Caucal Hierarchy / 3.5.1：
Two-Way Alternating Tree Automata / 3.5.2：
Morphic Trees / 3.5.3：
Layered Temporal Structures / 3.5.4：
Summary / 3.6：
Technical Proofs / A：
Proofs of Theorem 5 and Theorem 6 / A.l：
Proof of Theorem 8 / A.2：
Proof of Proposition 34 / A.3：
References
Notation
Index
Introduction / 1：
Word Automata and Time Granularities / 2：
Background Knowledge / 2.1：
7.

電子ブック
ＥＢ
7. On Probabilistic Conditional Independence Structures
Milan Studen?, Michael Jordan, Frank P. Kelly, Jon Kleinberg, Bernhard Sch?lkopf, Ian Witten
出版情報: Springer eBooks Computer Science , Springer London, 2005
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目次情報: 続きを見る
Introduction / 1：
Motivational thoughts / 1.1：
Goals of the monograph / 1.2：
Structure of the book / 1.3：
Basic Concepts / 2：
Conditional independence / 2.1：
Semi-graphoid properties / 2.2：
Formal independence models / 2.2.1：
Semi-graphoids / 2.2.2：
Elementary independence statements / 2.2.3：
Problem of axiomatic characterization / 2.2.4：
Classes of probability measures / 2.3：
Marginally continuous measures / 2.3.1：
Factorizable measures / 2.3.2：
Multiinformation and conditional product / 2.3.3：
Properties of multiinformation function / 2.3.4：
Positive measures / 2.3.5：
Gaussian measures / 2.3.6：
Basic construction / 2.3.7：
Imsets / 2.4：
Graphical Methods / 3：
Undirected graphs / 3.1：
Acyclic directed graphs / 3.2：
Classic chain graphs / 3.3：
Within classic graphical models / 3.4：
Decomposable models / 3.4.1：
Recursive causal graphs / 3.4.2：
Lattice conditional independence models / 3.4.3：
Bubble graphs / 3.4.4：
Advanced graphical models / 3.5：
General directed graphs / 3.5.1：
Reciprocal graphs / 3.5.2：
Joint-response chain graphs / 3.5.3：
Covariance graphs / 3.5.4：
Alternative chain graphs / 3.5.5：
Annotated graphs / 3.5.6：
Hidden variables / 3.5.7：
Ancestral graphs / 3.5.8：
MC graphs / 3.5.9：
Incompleteness of graphical approaches / 3.6：
Structural Imsets: Fundamentals / 4：
Basic class of distributions / 4.1：
Discrete measures / 4.1.1：
Regular Gaussian measures / 4.1.2：
Conditional Gaussian measures / 4.1.3：
Classes of structural imsets / 4.2：
Elementary imsets / 4.2.1：
Semi-elementary and combinatorial imsets / 4.2.2：
Structural imsets / 4.2.3：
Product formula induced by a structural imset / 4.3：
Examples of reference systems of measures / 4.3.1：
Topological assumptions / 4.3.2：
Markov condition / 4.4：
Semi-graphoid induced by a structural imset / 4.4.1：
Markovian measures / 4.4.2：
Equivalence result / 4.5：
Description of Probabilistic Models / 5：
Supermodular set functions / 5.1：
Semi-graphoid produced by a supermodular function / 5.1.1：
Quantitative equivalence of supermodular functions / 5.1.2：
Skeletal supermodular functions / 5.2：
Skeleton / 5.2.1：
Significance of skeletal imsets / 5.2.2：
Description of models by structural imsets / 5.3：
Galois connection / 5.4：
Formal concept analysis / 5.4.1：
Lattice of structural models / 5.4.2：
Equivalence and Implication / 6：
Two concepts of equivalence / 6.1：
Independence and Markov equivalence / 6.1.1：
Independence implication / 6.2：
Direct characterization of independence implication / 6.2.1：
Skeletal characterization of independence implication / 6.2.2：
Testing independence implication / 6.3：
Testing structural imsets / 6.3.1：
Grade / 6.3.2：
Invariants of independence equivalence / 6.4：
Adaptation to a distribution framework / 6.5：
The Problem of Representative Choice / 7：
Baricentral imsets / 7.1：
Standard imsets / 7.2：
Translation of DAG models / 7.2.1：
Translation of decomposable models / 7.2.2：
Imsets of the smallest degree / 7.3：
Decomposition implication / 7.3.1：
Minimal generators / 7.3.2：
Span / 7.4：
Determining and unimarginal classes / 7.4.1：
Imsets with the least lower class / 7.4.2：
Exclusivity of standard imsets / 7.4.3：
Dual description / 7.5：
Coportraits / 7.5.1：
Dual baricentral imsets and global view / 7.5.2：
Learning / 8：
Two approaches to learning / 8.1：
Quality criteria / 8.2：
Criteria for learning DAG models / 8.2.1：
Score equivalent criteria / 8.2.2：
Decomposable criteria / 8.2.3：
Regular criteria / 8.2.4：
Inclusion neighborhood / 8.3：
Standard imsets and learning / 8.4：
Inclusion neighborhood characterization / 8.4.1：
Regular criteria and standard imsets / 8.4.2：
Open Problems / 9：
Theoretical problems / 9.1：
Miscellaneous topics / 9.1.1：
Classification of skeletal imsets / 9.1.2：
Operations with structural models / 9.2：
Reductive operations / 9.2.1：
Expansive operations / 9.2.2：
Cumulative operations / 9.2.3：
Decomposition of structural models / 9.2.4：
Implementation tasks / 9.3：
Interpretation and learning tasks / 9.4：
Meaningful description of structural models / 9.4.1：
Tasks concerning distribution frameworks / 9.4.2：
Learning tasks / 9.4.3：
Appendix / A：
Classes of sets / A.1：
Posets and lattices / A.2：
Graphs / A.3：
Topological concepts / A.4：
Finite-dimensional subspaces and convex cones / A.5：
Linear subspaces / A.5.1：
Convex sets and cones / A.5.2：
Measure-theoretical concepts / A.6：
Measure and integral / A.6.1：
Basic measure-theoretical results / A.6.2：
Information-theoretical concepts / A.6.3：
Conditional probability / A.6.4：
Conditional independence in terms of ?-algebras / A.7：
Concepts from multivariate analysis / A.8：
Matrices / A.8.1：
Statistical characteristics of probability measures / A.8.2：
Multivariate Gaussian distributions / A.8.3：
Elementary statistical concepts / A.9：
Empirical concepts / A.9.1：
Statistical conception / A.9.2：
Likelihood function / A.9.3：
Testing statistical hypotheses / A.9.4：
Distribution framework / A.9.5：
List of Notation
List of Lemmas, Propositions etc
References
Index
Introduction / 1：
Motivational thoughts / 1.1：
Goals of the monograph / 1.2：
8.

電子ブック
ＥＢ
8. On Probabilistic Conditional Independence Structures
Milan Studený, Michael Jordan, Frank P. Kelly, Jon Kleinberg, Bernhard Schölkopf, Ian Witten
出版情報: SpringerLink Books - AutoHoldings , Springer London, 2005
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1：
Motivational thoughts / 1.1：
Goals of the monograph / 1.2：
Structure of the book / 1.3：
Basic Concepts / 2：
Conditional independence / 2.1：
Semi-graphoid properties / 2.2：
Formal independence models / 2.2.1：
Semi-graphoids / 2.2.2：
Elementary independence statements / 2.2.3：
Problem of axiomatic characterization / 2.2.4：
Classes of probability measures / 2.3：
Marginally continuous measures / 2.3.1：
Factorizable measures / 2.3.2：
Multiinformation and conditional product / 2.3.3：
Properties of multiinformation function / 2.3.4：
Positive measures / 2.3.5：
Gaussian measures / 2.3.6：
Basic construction / 2.3.7：
Imsets / 2.4：
Graphical Methods / 3：
Undirected graphs / 3.1：
Acyclic directed graphs / 3.2：
Classic chain graphs / 3.3：
Within classic graphical models / 3.4：
Decomposable models / 3.4.1：
Recursive causal graphs / 3.4.2：
Lattice conditional independence models / 3.4.3：
Bubble graphs / 3.4.4：
Advanced graphical models / 3.5：
General directed graphs / 3.5.1：
Reciprocal graphs / 3.5.2：
Joint-response chain graphs / 3.5.3：
Covariance graphs / 3.5.4：
Alternative chain graphs / 3.5.5：
Annotated graphs / 3.5.6：
Hidden variables / 3.5.7：
Ancestral graphs / 3.5.8：
MC graphs / 3.5.9：
Incompleteness of graphical approaches / 3.6：
Structural Imsets: Fundamentals / 4：
Basic class of distributions / 4.1：
Discrete measures / 4.1.1：
Regular Gaussian measures / 4.1.2：
Conditional Gaussian measures / 4.1.3：
Classes of structural imsets / 4.2：
Elementary imsets / 4.2.1：
Semi-elementary and combinatorial imsets / 4.2.2：
Structural imsets / 4.2.3：
Product formula induced by a structural imset / 4.3：
Examples of reference systems of measures / 4.3.1：
Topological assumptions / 4.3.2：
Markov condition / 4.4：
Semi-graphoid induced by a structural imset / 4.4.1：
Markovian measures / 4.4.2：
Equivalence result / 4.5：
Description of Probabilistic Models / 5：
Supermodular set functions / 5.1：
Semi-graphoid produced by a supermodular function / 5.1.1：
Quantitative equivalence of supermodular functions / 5.1.2：
Skeletal supermodular functions / 5.2：
Skeleton / 5.2.1：
Significance of skeletal imsets / 5.2.2：
Description of models by structural imsets / 5.3：
Galois connection / 5.4：
Formal concept analysis / 5.4.1：
Lattice of structural models / 5.4.2：
Equivalence and Implication / 6：
Two concepts of equivalence / 6.1：
Independence and Markov equivalence / 6.1.1：
Independence implication / 6.2：
Direct characterization of independence implication / 6.2.1：
Skeletal characterization of independence implication / 6.2.2：
Testing independence implication / 6.3：
Testing structural imsets / 6.3.1：
Grade / 6.3.2：
Invariants of independence equivalence / 6.4：
Adaptation to a distribution framework / 6.5：
The Problem of Representative Choice / 7：
Baricentral imsets / 7.1：
Standard imsets / 7.2：
Translation of DAG models / 7.2.1：
Translation of decomposable models / 7.2.2：
Imsets of the smallest degree / 7.3：
Decomposition implication / 7.3.1：
Minimal generators / 7.3.2：
Span / 7.4：
Determining and unimarginal classes / 7.4.1：
Imsets with the least lower class / 7.4.2：
Exclusivity of standard imsets / 7.4.3：
Dual description / 7.5：
Coportraits / 7.5.1：
Dual baricentral imsets and global view / 7.5.2：
Learning / 8：
Two approaches to learning / 8.1：
Quality criteria / 8.2：
Criteria for learning DAG models / 8.2.1：
Score equivalent criteria / 8.2.2：
Decomposable criteria / 8.2.3：
Regular criteria / 8.2.4：
Inclusion neighborhood / 8.3：
Standard imsets and learning / 8.4：
Inclusion neighborhood characterization / 8.4.1：
Regular criteria and standard imsets / 8.4.2：
Open Problems / 9：
Theoretical problems / 9.1：
Miscellaneous topics / 9.1.1：
Classification of skeletal imsets / 9.1.2：
Operations with structural models / 9.2：
Reductive operations / 9.2.1：
Expansive operations / 9.2.2：
Cumulative operations / 9.2.3：
Decomposition of structural models / 9.2.4：
Implementation tasks / 9.3：
Interpretation and learning tasks / 9.4：
Meaningful description of structural models / 9.4.1：
Tasks concerning distribution frameworks / 9.4.2：
Learning tasks / 9.4.3：
Appendix / A：
Classes of sets / A.1：
Posets and lattices / A.2：
Graphs / A.3：
Topological concepts / A.4：
Finite-dimensional subspaces and convex cones / A.5：
Linear subspaces / A.5.1：
Convex sets and cones / A.5.2：
Measure-theoretical concepts / A.6：
Measure and integral / A.6.1：
Basic measure-theoretical results / A.6.2：
Information-theoretical concepts / A.6.3：
Conditional probability / A.6.4：
Conditional independence in terms of ?-algebras / A.7：
Concepts from multivariate analysis / A.8：
Matrices / A.8.1：
Statistical characteristics of probability measures / A.8.2：
Multivariate Gaussian distributions / A.8.3：
Elementary statistical concepts / A.9：
Empirical concepts / A.9.1：
Statistical conception / A.9.2：
Likelihood function / A.9.3：
Testing statistical hypotheses / A.9.4：
Distribution framework / A.9.5：
List of Notation
List of Lemmas, Propositions etc
References
Index
Introduction / 1：
Motivational thoughts / 1.1：
Goals of the monograph / 1.2：
9.

電子ブック
ＥＢ
9. Sensitivity Analysis for Neural Networks
Daniel S. Yeung, Ian Cloete, Wing W. Y. Ng, Daming Shi
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2010
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目次情報: 続きを見る
Introduction to Neural Networks / 1：
Properties of Neural Networks / 1.1：
Neural Network Learning / 1.2：
Supervised Learning / 1.2.1：
Unsupervised Learning / 1.2.2：
Perceptron / 1.3：
Adaline and Least Mean Square Algorithm / 1.4：
Multilayer Perceptron and Backpropagation Algorithm / 1.5：
Output Layer Learning / 1.5.1：
Hidden Layer Learning / 1.5.2：
Radial Basis Function Networks / 1.6：
Support Vector Machines / 1.7：
Principles of Sensitivity Analysis / 2：
Perturbations in Neural Networks / 2.1：
Neural Network Sensitivity Analysis / 2.2：
Fundamental Methods of Sensitivity Analysis / 2.3：
Geometrical Approach / 2.3.1：
Statistical Approach / 2.3.2：
Summary / 2.4：
Hyper-Rectangle Model / 3：
Hyper-Rectangle Model for Input Space of MLP / 3.1：
Sensitivity Measure of MLP / 3.2：
Discussion / 3.3：
Sensitivity Analysis with Parameterized Activation Function / 4：
Parameterized Antisymmetric Squashing Function / 4.1：
Sensitivity Measure / 4.2：
Localized Generalization Error Model / 4.3：
Introduction / 5.1：
The Localized Generalization Error Model / 5.2：
The Q-Neighborhood and Q-Union / 5.2.1：
The Localized Generalization Error Bound / 5.2.2：
Stochastic Sensitivity Measure for RBFNN / 5.2.3：
Characteristics of the Error Bound / 5.2.4：
Comparing Two Classifiers Using the Error Bound / 5.2.5：
Architecture Selection Using the Error Bound / 5.3：
Critical Vector Learning for RBF Networks / 5.3.1：
Related Work / 6.1：
Construction of RBF Networks with Sensitivity Analysis / 6.2：
RBF Classifiers' Sensitivity to the Kernel Function Centers / 6.2.1：
Orthogonal Least Square Transform / 6.2.2：
Critical Vector Selection / 6.2.3：
Sensitivity Analysis of Prior Knowledge / 6.3：
KBANNs / 7.1：
Inductive Bias / 7.2：
Sensitivity Analysis and Measures / 7.3：
Output-Pattern Sensitivity / 7.3.1：
Output-Weight Sensitivity / 7.3.2：
Output-H Sensitivity / 7.3.3：
Euclidean Distance / 7.3.4：
Promoter Recognition / 7.4：
Data and Initial Domain Theory / 7.4.1：
Experimental Methodology / 7.4.2：
Discussion and Conclusion / 7.5：
Applications / 8：
Input Dimension Reduction / 8.1：
Sensitivity Matrix / 8.1.1：
Criteria for Pruning Inputs / 8.1.2：
Network Optimization / 8.2：
Selective Learning / 8.3：
Hardware Robustness / 8.4：
Measure of Nonlinearity / 8.5：
Parameter Tuning for Neocognitron / 8.6：
Receptive Field / 8.6.1：
Selectivity / 8.6.2：
Sensitivity Analysis of the Neocognitron / 8.6.3：
Bibliography
Introduction to Neural Networks / 1：
Properties of Neural Networks / 1.1：
Neural Network Learning / 1.2：
10.

電子ブック
ＥＢ
10. Sensitivity Analysis for Neural Networks
Daniel S. Yeung, Ian Cloete, Wing W. Y. Ng, Daming Shi
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2010
所蔵情報: loading…
目次情報: 続きを見る
Introduction to Neural Networks / 1：
Properties of Neural Networks / 1.1：
Neural Network Learning / 1.2：
Supervised Learning / 1.2.1：
Unsupervised Learning / 1.2.2：
Perceptron / 1.3：
Adaline and Least Mean Square Algorithm / 1.4：
Multilayer Perceptron and Backpropagation Algorithm / 1.5：
Output Layer Learning / 1.5.1：
Hidden Layer Learning / 1.5.2：
Radial Basis Function Networks / 1.6：
Support Vector Machines / 1.7：
Principles of Sensitivity Analysis / 2：
Perturbations in Neural Networks / 2.1：
Neural Network Sensitivity Analysis / 2.2：
Fundamental Methods of Sensitivity Analysis / 2.3：
Geometrical Approach / 2.3.1：
Statistical Approach / 2.3.2：
Summary / 2.4：
Hyper-Rectangle Model / 3：
Hyper-Rectangle Model for Input Space of MLP / 3.1：
Sensitivity Measure of MLP / 3.2：
Discussion / 3.3：
Sensitivity Analysis with Parameterized Activation Function / 4：
Parameterized Antisymmetric Squashing Function / 4.1：
Sensitivity Measure / 4.2：
Localized Generalization Error Model / 4.3：
Introduction / 5.1：
The Localized Generalization Error Model / 5.2：
The Q-Neighborhood and Q-Union / 5.2.1：
The Localized Generalization Error Bound / 5.2.2：
Stochastic Sensitivity Measure for RBFNN / 5.2.3：
Characteristics of the Error Bound / 5.2.4：
Comparing Two Classifiers Using the Error Bound / 5.2.5：
Architecture Selection Using the Error Bound / 5.3：
Critical Vector Learning for RBF Networks / 5.3.1：
Related Work / 6.1：
Construction of RBF Networks with Sensitivity Analysis / 6.2：
RBF Classifiers' Sensitivity to the Kernel Function Centers / 6.2.1：
Orthogonal Least Square Transform / 6.2.2：
Critical Vector Selection / 6.2.3：
Sensitivity Analysis of Prior Knowledge / 6.3：
KBANNs / 7.1：
Inductive Bias / 7.2：
Sensitivity Analysis and Measures / 7.3：
Output-Pattern Sensitivity / 7.3.1：
Output-Weight Sensitivity / 7.3.2：
Output-H Sensitivity / 7.3.3：
Euclidean Distance / 7.3.4：
Promoter Recognition / 7.4：
Data and Initial Domain Theory / 7.4.1：
Experimental Methodology / 7.4.2：
Discussion and Conclusion / 7.5：
Applications / 8：
Input Dimension Reduction / 8.1：
Sensitivity Matrix / 8.1.1：
Criteria for Pruning Inputs / 8.1.2：
Network Optimization / 8.2：
Selective Learning / 8.3：
Hardware Robustness / 8.4：
Measure of Nonlinearity / 8.5：
Parameter Tuning for Neocognitron / 8.6：
Receptive Field / 8.6.1：
Selectivity / 8.6.2：
Sensitivity Analysis of the Neocognitron / 8.6.3：
Bibliography
Introduction to Neural Networks / 1：
Properties of Neural Networks / 1.1：
Neural Network Learning / 1.2：
11.

電子ブック
ＥＢ
11. Pedagogically Founded Courseware Generation for Web-Based Learning
Carsten Ullrich, J?rg Siekmann
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2008
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目次情報: 続きを見る
Preliminaries / Part I：
Introduction / 1：
Motivation / 1.1：
Contributions / 1.2：
Service-Oriented Course Generation / 1.2.1：
Modeling of Pedagogical Knowledge / 1.2.2：
Adaptivity in Generated Courses / 1.2.3：
Evaluation / 1.2.4：
Overview / 1.3：
Relevant Technologies / 2：
Basic Terminology / 2.1：
Semantic Web Technologies / 2.2：
Extensible Markup Language / 2.2.1：
Resource Description Framework / 2.2.2：
OWL Web Ontology Language / 2.2.3：
E-learning Standards / 2.3：
Learning Object Metadata / 2.3.1：
IMS Content Packaging / 2.3.2：
IMS Simple Sequencing / 2.3.3：
IMS Learning Design / 2.3.4：
Mathematics in the Web / 2.4：
OMDoc (Open Mathematical Documents) / 2.4.1：
The Learning Environment ActiveMath / 2.4.2：
Course Generation / 2.5：
Hierarchical Task Network Planning / 2.6：
Introduction to AI-Planning / 2.6.1：
Introduction to Hierarchical Task Network Planning / 2.6.2：
SHOP2 and JSHOP2 / 2.6.3：
JSHOP2 Formalism / 2.6.4：
Descriptive and Prescriptive Learning Theories / 3：
Behaviorism / 3.1：
Cognitivism / 3.2：
Constructivism / 3.3：
Instructional Design / 3.4：
Competency-Based Learning / 3.5：
Mathematical Competencies / 3.5.1：
Competency Levels / 3.5.2：
PAIGOS / Part II：
General Principles / 4：
An Ontology of Instructional Objects / 4.1：
Description of the Ontology / 4.1.1：
Why an Ontology? / 4.1.3：
Applications of the Ontology / 4.1.4：
A Mediator for Accessing Learning Object Repositories / 4.2：
Related Work / 4.2.1：
Overview of the Mediator Architecture / 4.2.2：
Querying the Mediator / 4.2.3：
Ontology Mapping and Query Rewriting / 4.2.4：
Repository Interface and Caching / 4.2.5：
Limitations of the Mediator as an Educational Service / 4.2.6：
Pedagogical Tasks, Methods and Strategies / 4.3：
Representing Course Generation Knowledge in an HTN Planner / 4.4：
Mapping Pedagogical Tasks onto HTN Tasks / 4.4.1：
Course Generation Planning Problems / 4.4.3：
Critical and Optional Tasks / 4.4.4：
Basic General Purpose Axioms and Operators / 4.5：
Testing for Equality / 4.5.1：
List Manipulation / 4.5.2：
Binding a Variable to All Terms of a Term List / 4.5.3：
Manipulating the World State / 4.5.4：
Basic Operators and Methods of the Course Generation Domain / 4.6：
Inserting References to Educational Resources / 4.6.1：
Starting and Ending Sections / 4.6.2：
Inserting References to Learning-Support Services / 4.6.3：
An Operator for Dynamic Text Generation / 4.6.4：
Dynamic Subtask Expansion / 4.6.5：
Accessing Information about Educational Resources / 4.6.6：
Axioms for Accessing the Learner Model / 4.6.7：
Processing Resources Depending on Learner Characteristics / 4.6.8：
Initializing and Manipulating Information about the Learning Goal / 4.6.9：
Converting a Plan into a Course / 4.7：
Generating Structure and Adaptivity: Dynamic Tasks / 4.8：
Generation of Narrative Bridges and Structure / 4.9：
Empirical Findings / 4.9.1：
Operator and Methods for Text Generation / 4.9.2：
Symbolic Representations of Dynamic Text Items / 4.9.3：
Generation of Structure Information / 4.9.4：
Summary / 4.10：
Course Generation in Practice: Formalized Scenarios / 5：
Moderate Constructivist Competency-Based Scenarios / 5.1：
Course Generation and Constructivism - a Contradiction? / 5.1.1：
Selecting Exercises / 5.1.2：
Selecting Examples / 5.1.3：
Scenario "Discover" / 5.1.4：
Scenario "Rehearse" / 5.1.5：
Scenario "Connect" / 5.1.6：
Scenario "Train Intensively" / 5.1.7：
Scenario "Train Competencies" / 5.1.8：
Scenario "Exam Simulation" / 5.1.9：
Course Generation Based on Instructional Design Principles / 5.2：
Merrill's "First Principles of Instruction" / 5.2.1：
Scenario "Guided Tour" / 5.2.2：
Implementation and Integration / 6：
Implementation / 6.1：
Integration of PAIGOS in ActiveMath / 6.2：
Course Generation in ActiveMath / 6.2.1：
Dynamically Generated Elements in a Table of Contents / 6.2.2：
Usage of Learning-Support Services in ActiveMath / 6.2.3：
Template-Based Generation of Narrative Bridges / 6.2.4：
PAIGOS as a Service in ActiveMath / 6.2.5：
Course Generation as a Web-Service / 6.3：
Interfaces / 6.3.1：
Technical Evaluations and Use Cases / 7：
Evaluation of the Ontology / 7.1.1：
Mediator Use Cases and Evaluations / 7.1.2：
Course Generation Use Cases and Evaluations / 7.1.3：
Performance of PAIGOS / 7.1.4：
Discussion / 7.1.5：
Formative and Summative Evaluation / 7.2：
Formative Evaluations / 7.2.1：
Summative Evaluation / 7.2.2：
Conclusions / 7.2.3：
Early Work / 8：
Generic Tutoring Environment / 8.2：
Dynamic Courseware Generator / 8.3：
ACE/WINDS / 8.4：
Former Course Generator of ActiveMath / 8.5：
APeLS/iClass / 8.6：
SeLeNe / 8.7：
Statistical Methods for Course Generation / 8.8：
Approaches Using Hierarchical Task Network Planning / 8.9：
Ontologies for Instructional Design / 8.10：
Future Work and Acknowledgments / 9：
Future Work / 9.1：
Complete List of User Comments
References
Index
Preliminaries / Part I：
Introduction / 1：
Motivation / 1.1：
12.

電子ブック
ＥＢ
12. Pedagogically Founded Courseware Generation for Web-Based Learning
Carsten Ullrich, Jörg Siekmann
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2008
所蔵情報: loading…
目次情報: 続きを見る
Preliminaries / Part I：
Introduction / 1：
Motivation / 1.1：
Contributions / 1.2：
Service-Oriented Course Generation / 1.2.1：
Modeling of Pedagogical Knowledge / 1.2.2：
Adaptivity in Generated Courses / 1.2.3：
Evaluation / 1.2.4：
Overview / 1.3：
Relevant Technologies / 2：
Basic Terminology / 2.1：
Semantic Web Technologies / 2.2：
Extensible Markup Language / 2.2.1：
Resource Description Framework / 2.2.2：
OWL Web Ontology Language / 2.2.3：
E-learning Standards / 2.3：
Learning Object Metadata / 2.3.1：
IMS Content Packaging / 2.3.2：
IMS Simple Sequencing / 2.3.3：
IMS Learning Design / 2.3.4：
Mathematics in the Web / 2.4：
OMDoc (Open Mathematical Documents) / 2.4.1：
The Learning Environment ActiveMath / 2.4.2：
Course Generation / 2.5：
Hierarchical Task Network Planning / 2.6：
Introduction to AI-Planning / 2.6.1：
Introduction to Hierarchical Task Network Planning / 2.6.2：
SHOP2 and JSHOP2 / 2.6.3：
JSHOP2 Formalism / 2.6.4：
Descriptive and Prescriptive Learning Theories / 3：
Behaviorism / 3.1：
Cognitivism / 3.2：
Constructivism / 3.3：
Instructional Design / 3.4：
Competency-Based Learning / 3.5：
Mathematical Competencies / 3.5.1：
Competency Levels / 3.5.2：
PAIGOS / Part II：
General Principles / 4：
An Ontology of Instructional Objects / 4.1：
Description of the Ontology / 4.1.1：
Why an Ontology? / 4.1.3：
Applications of the Ontology / 4.1.4：
A Mediator for Accessing Learning Object Repositories / 4.2：
Related Work / 4.2.1：
Overview of the Mediator Architecture / 4.2.2：
Querying the Mediator / 4.2.3：
Ontology Mapping and Query Rewriting / 4.2.4：
Repository Interface and Caching / 4.2.5：
Limitations of the Mediator as an Educational Service / 4.2.6：
Pedagogical Tasks, Methods and Strategies / 4.3：
Representing Course Generation Knowledge in an HTN Planner / 4.4：
Mapping Pedagogical Tasks onto HTN Tasks / 4.4.1：
Course Generation Planning Problems / 4.4.3：
Critical and Optional Tasks / 4.4.4：
Basic General Purpose Axioms and Operators / 4.5：
Testing for Equality / 4.5.1：
List Manipulation / 4.5.2：
Binding a Variable to All Terms of a Term List / 4.5.3：
Manipulating the World State / 4.5.4：
Basic Operators and Methods of the Course Generation Domain / 4.6：
Inserting References to Educational Resources / 4.6.1：
Starting and Ending Sections / 4.6.2：
Inserting References to Learning-Support Services / 4.6.3：
An Operator for Dynamic Text Generation / 4.6.4：
Dynamic Subtask Expansion / 4.6.5：
Accessing Information about Educational Resources / 4.6.6：
Axioms for Accessing the Learner Model / 4.6.7：
Processing Resources Depending on Learner Characteristics / 4.6.8：
Initializing and Manipulating Information about the Learning Goal / 4.6.9：
Converting a Plan into a Course / 4.7：
Generating Structure and Adaptivity: Dynamic Tasks / 4.8：
Generation of Narrative Bridges and Structure / 4.9：
Empirical Findings / 4.9.1：
Operator and Methods for Text Generation / 4.9.2：
Symbolic Representations of Dynamic Text Items / 4.9.3：
Generation of Structure Information / 4.9.4：
Summary / 4.10：
Course Generation in Practice: Formalized Scenarios / 5：
Moderate Constructivist Competency-Based Scenarios / 5.1：
Course Generation and Constructivism - a Contradiction? / 5.1.1：
Selecting Exercises / 5.1.2：
Selecting Examples / 5.1.3：
Scenario "Discover" / 5.1.4：
Scenario "Rehearse" / 5.1.5：
Scenario "Connect" / 5.1.6：
Scenario "Train Intensively" / 5.1.7：
Scenario "Train Competencies" / 5.1.8：
Scenario "Exam Simulation" / 5.1.9：
Course Generation Based on Instructional Design Principles / 5.2：
Merrill's "First Principles of Instruction" / 5.2.1：
Scenario "Guided Tour" / 5.2.2：
Implementation and Integration / 6：
Implementation / 6.1：
Integration of PAIGOS in ActiveMath / 6.2：
Course Generation in ActiveMath / 6.2.1：
Dynamically Generated Elements in a Table of Contents / 6.2.2：
Usage of Learning-Support Services in ActiveMath / 6.2.3：
Template-Based Generation of Narrative Bridges / 6.2.4：
PAIGOS as a Service in ActiveMath / 6.2.5：
Course Generation as a Web-Service / 6.3：
Interfaces / 6.3.1：
Technical Evaluations and Use Cases / 7：
Evaluation of the Ontology / 7.1.1：
Mediator Use Cases and Evaluations / 7.1.2：
Course Generation Use Cases and Evaluations / 7.1.3：
Performance of PAIGOS / 7.1.4：
Discussion / 7.1.5：
Formative and Summative Evaluation / 7.2：
Formative Evaluations / 7.2.1：
Summative Evaluation / 7.2.2：
Conclusions / 7.2.3：
Early Work / 8：
Generic Tutoring Environment / 8.2：
Dynamic Courseware Generator / 8.3：
ACE/WINDS / 8.4：
Former Course Generator of ActiveMath / 8.5：
APeLS/iClass / 8.6：
SeLeNe / 8.7：
Statistical Methods for Course Generation / 8.8：
Approaches Using Hierarchical Task Network Planning / 8.9：
Ontologies for Instructional Design / 8.10：
Future Work and Acknowledgments / 9：
Future Work / 9.1：
Complete List of User Comments
References
Index
Preliminaries / Part I：
Introduction / 1：
Motivation / 1.1：
13.

電子ブック
ＥＢ
13. Architecture-Based Design of Multi-Agent Systems
Danny Weyns
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2010
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目次情報: 続きを見る
Introduction / 1：
Software Architecture and Middleware / 1.1：
Software Architecture / 1.1.1：
Middleware / 1.1.2：
Agent-Oriented Methodologies / 1.2：
Case Study / 1.3：
Overview of the Book / 1.4：
Overview of Architecture-Based Design of Multi-Agent Systems / 2：
General Overview of the Approach / 2.1：
Architectural Design in the Development Life Cycle / 2.1.1：
Steps of Architecture-Based Design of Multi-Agent Systems / 2.1.2：
Functional and Quality Attribute Requirements / 2.2：
Architectural Design / 2.3：
Architectural Patterns / 2.3.1：
ADD Process / 2.3.2：
Middleware Support for Multi-Agent Systems / 2.4：
Documenting Software Architecture / 2.5：
Architectural Views / 2.5.1：
Architectural Description Languages / 2.5.2：
Evaluating Software Architecture / 2.6：
From Software Architecture to Downstream Design and Implementation / 2.7：
Summary / 2.8：
Capturing Expertise in Multi-Agent System Engineering with Architectural Patterns / 3：
Situated Multi-Agent Systems / 3.1：
Single-Agent Systems / 3.1.1：
Multi-Agent Systems / 3.1.2：
Target Domain of the Pattern Language for Situated Multi-Agent Systems / 3.2：
Overview of the Pattern Language / 3.3：
Pattern Template / 3.4：
Virtual Environment / 3.5：
Primary Presentation / 3.5.1：
Architectural Elements / 3.5.2：
Interface Descriptions / 3.5.3：
Design Rationale / 3.5.4：
Situated Agent / 3.6：
Selective Perception / 3.6.1：
Roles and Situated Commitments / 3.7.1：
Free-Flow Trees Extended with Roles and Situated Commitments / 3.8.1：
Protocol-Based Communication / 3.9：
Architectural Design of Multi-Agent Systems / 3.9.1：
Designing and Documenting Multi-Agent System Architectures / 4.1：
Designing and Documenting Architecture in the Development Life Cycle / 4.1.1：
Inputs and Outputs of ADD / 4.1.2：
Overview of the ADD Activities / 4.1.3：
The Domain of Automated Transportation Systems / 4.2：
Business Case / 4.2.2：
System Requirements / 4.2.3：
General Overview of the Design / 4.3：
Challenges at the Outset / 4.3.1：
The System and Its Environment / 4.3.2：
Design Process / 4.3.3：
High-Level Design / 4.3.4：
Architecture Documentation / 4.4：
Introduction to the Architecture Documentation / 4.4.1：
Deployment View / 4.4.2：
Module Uses View / 4.4.3：
Collaborating Components View / 4.4.4：
Middleware for Distributed Multi-Agent Systems / 4.5：
Middleware Support for Distributed, Decentralized Coordination / 5.1：
Middleware in Distributed Software Systems / 5.1.1：
Middleware in Multi-Agent Systems / 5.1.2：
Scope of the Middleware and Requirements / 5.2：
Objectplaces / 5.2.2：
Views / 5.2.3：
Coordination Roles / 5.2.4：
Middleware Architecture / 5.3：
High-Level Module Decomposition / 5.3.1：
Group Formation / 5.3.2：
View Management / 5.3.3：
Role Activation / 5.3.4：
Collision Avoidance in the AGV Transportation System / 5.4：
Collision Avoidance / 5.4.1：
Collision Avoidance Protocol / 5.4.2：
Software Architecture: Communicating Processes for Collision Avoidance / 5.4.3：
Task Assignment / 5.5：
Schedule-Based Task Assignment / 6.1：
FiTA: Field-Based Task Assignment / 6.2：
Coordination Fields / 6.2.1：
Adaptive Task Assignment / 6.2.2：
Dealing With Local Minima / 6.2.3：
DynCNET Protocol / 6.3：
Monitoring the Area of Interest / 6.3.1：
Convergence / 6.3.3：
Synchronization Issues / 6.3.4：
Evaluation / 6.4：
Test Setting / 6.4.1：
Test Results / 6.4.2：
Tradeoff Analysis / 6.4.3：
Evaluation of Multi-Agent System Architectures / 6.5：
Evaluating Multi-Agent System Architectures with ATAM / 7.1：
Architecture Evaluation in the Development Life Cycle / 7.1.1：
Objectives of a Multi-Agent System Architecture Evaluation / 7.1.2：
Overview of the ATAM Activities / 7.1.3：
AGV Transportation System for a Tea Processing Warehouse / 7.2：
Evaluation Process / 7.2.2：
Quality Attribute Workshop / 7.2.3：
Analysis of Architectural Approaches / 7.2.4：
Reflection on ATAM for Evaluating a Multi-Agent System Architecture / 7.3：
ATAM Follow-Up and Demonstrator / 7.4：
Related Approaches / 7.5：
Architectural Approaches and Multi-Agent Systems / 8.1：
Architectural Styles / 8.1.1：
Reference Models and Architectures for Multi-Agent Systems / 8.1.2：
Middleware for Mobile Systems / 8.2：
Work Related to Views / 8.2.1：
Work Related to Coordination Roles / 8.2.2：
Scheduling and Routing of AGV Transportation Systems / 8.3：
AI and Robotics Approaches / 8.3.1：
Multi-Agent System Approaches / 8.3.2：
Conclusions / 9：
Reflection on Architecture-Based Design of Multi-Agent Systems / 9.1：
It Works! / 9.1.1：
Reflection on the Project with Egemin / 9.1.2：
Lessons Learned and Challenges / 9.2：
Dealing with Quality Attributes / 9.2.1：
Designing a Multi-Agent System Architecture / 9.2.2：
Integrating a Multi-Agent System with Its Software Environment / 9.2.3：
Impact of Adopting a Multi-Agent System / 9.2.4：
?-ADL Specification of the Architectural Patterns / A：
Language Constructs / A.1：
Virtual Environment Pattern / A.2：
Situated Agent Pattern / A.3：
Synchronization in the DynCNET Protocol / B：
Synchronization of Abort and Bound Messages / B.1：
Synchronization of Scope Dynamics / B.2：
Overview / C：
Invariant / C.2：
Maintaining the Invariant / C.3：
Glossary
References
Index
Introduction / 1：
Software Architecture and Middleware / 1.1：
Software Architecture / 1.1.1：
14.

電子ブック
ＥＢ
14. Architecture-Based Design of Multi-Agent Systems
Danny Weyns
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2010
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1：
Software Architecture and Middleware / 1.1：
Software Architecture / 1.1.1：
Middleware / 1.1.2：
Agent-Oriented Methodologies / 1.2：
Case Study / 1.3：
Overview of the Book / 1.4：
Overview of Architecture-Based Design of Multi-Agent Systems / 2：
General Overview of the Approach / 2.1：
Architectural Design in the Development Life Cycle / 2.1.1：
Steps of Architecture-Based Design of Multi-Agent Systems / 2.1.2：
Functional and Quality Attribute Requirements / 2.2：
Architectural Design / 2.3：
Architectural Patterns / 2.3.1：
ADD Process / 2.3.2：
Middleware Support for Multi-Agent Systems / 2.4：
Documenting Software Architecture / 2.5：
Architectural Views / 2.5.1：
Architectural Description Languages / 2.5.2：
Evaluating Software Architecture / 2.6：
From Software Architecture to Downstream Design and Implementation / 2.7：
Summary / 2.8：
Capturing Expertise in Multi-Agent System Engineering with Architectural Patterns / 3：
Situated Multi-Agent Systems / 3.1：
Single-Agent Systems / 3.1.1：
Multi-Agent Systems / 3.1.2：
Target Domain of the Pattern Language for Situated Multi-Agent Systems / 3.2：
Overview of the Pattern Language / 3.3：
Pattern Template / 3.4：
Virtual Environment / 3.5：
Primary Presentation / 3.5.1：
Architectural Elements / 3.5.2：
Interface Descriptions / 3.5.3：
Design Rationale / 3.5.4：
Situated Agent / 3.6：
Selective Perception / 3.6.1：
Roles and Situated Commitments / 3.7.1：
Free-Flow Trees Extended with Roles and Situated Commitments / 3.8.1：
Protocol-Based Communication / 3.9：
Architectural Design of Multi-Agent Systems / 3.9.1：
Designing and Documenting Multi-Agent System Architectures / 4.1：
Designing and Documenting Architecture in the Development Life Cycle / 4.1.1：
Inputs and Outputs of ADD / 4.1.2：
Overview of the ADD Activities / 4.1.3：
The Domain of Automated Transportation Systems / 4.2：
Business Case / 4.2.2：
System Requirements / 4.2.3：
General Overview of the Design / 4.3：
Challenges at the Outset / 4.3.1：
The System and Its Environment / 4.3.2：
Design Process / 4.3.3：
High-Level Design / 4.3.4：
Architecture Documentation / 4.4：
Introduction to the Architecture Documentation / 4.4.1：
Deployment View / 4.4.2：
Module Uses View / 4.4.3：
Collaborating Components View / 4.4.4：
Middleware for Distributed Multi-Agent Systems / 4.5：
Middleware Support for Distributed, Decentralized Coordination / 5.1：
Middleware in Distributed Software Systems / 5.1.1：
Middleware in Multi-Agent Systems / 5.1.2：
Scope of the Middleware and Requirements / 5.2：
Objectplaces / 5.2.2：
Views / 5.2.3：
Coordination Roles / 5.2.4：
Middleware Architecture / 5.3：
High-Level Module Decomposition / 5.3.1：
Group Formation / 5.3.2：
View Management / 5.3.3：
Role Activation / 5.3.4：
Collision Avoidance in the AGV Transportation System / 5.4：
Collision Avoidance / 5.4.1：
Collision Avoidance Protocol / 5.4.2：
Software Architecture: Communicating Processes for Collision Avoidance / 5.4.3：
Task Assignment / 5.5：
Schedule-Based Task Assignment / 6.1：
FiTA: Field-Based Task Assignment / 6.2：
Coordination Fields / 6.2.1：
Adaptive Task Assignment / 6.2.2：
Dealing With Local Minima / 6.2.3：
DynCNET Protocol / 6.3：
Monitoring the Area of Interest / 6.3.1：
Convergence / 6.3.3：
Synchronization Issues / 6.3.4：
Evaluation / 6.4：
Test Setting / 6.4.1：
Test Results / 6.4.2：
Tradeoff Analysis / 6.4.3：
Evaluation of Multi-Agent System Architectures / 6.5：
Evaluating Multi-Agent System Architectures with ATAM / 7.1：
Architecture Evaluation in the Development Life Cycle / 7.1.1：
Objectives of a Multi-Agent System Architecture Evaluation / 7.1.2：
Overview of the ATAM Activities / 7.1.3：
AGV Transportation System for a Tea Processing Warehouse / 7.2：
Evaluation Process / 7.2.2：
Quality Attribute Workshop / 7.2.3：
Analysis of Architectural Approaches / 7.2.4：
Reflection on ATAM for Evaluating a Multi-Agent System Architecture / 7.3：
ATAM Follow-Up and Demonstrator / 7.4：
Related Approaches / 7.5：
Architectural Approaches and Multi-Agent Systems / 8.1：
Architectural Styles / 8.1.1：
Reference Models and Architectures for Multi-Agent Systems / 8.1.2：
Middleware for Mobile Systems / 8.2：
Work Related to Views / 8.2.1：
Work Related to Coordination Roles / 8.2.2：
Scheduling and Routing of AGV Transportation Systems / 8.3：
AI and Robotics Approaches / 8.3.1：
Multi-Agent System Approaches / 8.3.2：
Conclusions / 9：
Reflection on Architecture-Based Design of Multi-Agent Systems / 9.1：
It Works! / 9.1.1：
Reflection on the Project with Egemin / 9.1.2：
Lessons Learned and Challenges / 9.2：
Dealing with Quality Attributes / 9.2.1：
Designing a Multi-Agent System Architecture / 9.2.2：
Integrating a Multi-Agent System with Its Software Environment / 9.2.3：
Impact of Adopting a Multi-Agent System / 9.2.4：
?-ADL Specification of the Architectural Patterns / A：
Language Constructs / A.1：
Virtual Environment Pattern / A.2：
Situated Agent Pattern / A.3：
Synchronization in the DynCNET Protocol / B：
Synchronization of Abort and Bound Messages / B.1：
Synchronization of Scope Dynamics / B.2：
Overview / C：
Invariant / C.2：
Maintaining the Invariant / C.3：
Glossary
References
Index
Introduction / 1：
Software Architecture and Middleware / 1.1：
Software Architecture / 1.1.1：
15.

電子ブック
ＥＢ
15. Machine Learning Techniques for Multimedia
D. M. Gabbay, Matthieu Cord, J. Siekmann
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2008
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Introduction to Learning Principles for Multimedia Data / Part I：
Introduction to Bayesian Methods and Decision Theory / Simon P. Wilson ; Rozenn Dahyot ; Padraig Cunningham1：
Introduction / 1.1：
Uncertainty and Probability / 1.2：
Quantifying Uncertainty / 1.2.1：
The Laws of Probability / 1.2.2：
Interpreting Probability / 1.2.3：
The Partition Law and Bayes' Law / 1.2.4：
Probability Models, Parameters and Likelihoods / 1.3：
Bayesian Statistical Learning / 1.4：
Implementing Bayesian Statistical Learning Methods / 1.5：
Direct Simulation Methods / 1.5.1：
Markov Chain Monte Carlo / 1.5.2：
Monte Carlo Integration / 1.5.3：
Optimization Methods / 1.5.4：
Decision Theory / 1.6：
Utility and Choosing the Optimal Decision / 1.6.1：
Where Is the Utility? / 1.6.2：
Native Bayes / 1.7：
Further Reading / 1.8：
References
Supervised Learning / Matthieu Cord ; Sarah Jane Delany2：
Introduction to Statistical Learning / 2.1：
Risk Minimization / 2.2.1：
Empirical Risk Minimization / 2.2.2：
Risk Bounds / 2.2.3：
Support Vector Machines and Kernels / 2.3：
Linear Classification: SVM Principle / 2.3.1：
Soft Margin / 2.3.2：
Kernel-Based Classification / 2.3.3：
Nearest Neighbour Classification / 2.4：
Similarity and Distance Metrics / 2.4.1：
Other Distance Metrics for Multimedia Data / 2.4.2：
Computational Complexity / 2.4.3：
Instance Selection and Noise Reduction / 2.4.4：
k-NN: Advantages and Disadvantages / 2.4.5：
Ensemble Techniques / 2.5：
Bias-Variance Analysis of Error / 2.5.1：
Bagging / 2.5.3：
Random Forests / 2.5.4：
Boosting / 2.5.5：
Summary / 2.6：
Unsupervised Learning and Clustering / Derek Greene ; Páadraig Cunningham ; Rudolf Mayer3：
Basic Clustering Techniques / 3.1：
k-Means Clustering / 3.2.1：
Fuzzy Clustering / 3.2.2：
Hierarchical Clustering / 3.2.3：
Modern Clustering Techniques / 3.3：
Kernel Clustering / 3.3.1：
Spectral Clustering / 3.3.2：
Self-organizing Maps / 3.4：
SOM Architecture / 3.4.1：
SOM Algorithm / 3.4.2：
Self-organizing Map and Clustering / 3.4.3：
Variations of the Self-organizing Map / 3.4.4：
Cluster Validation / 3.5：
Internal Validation / 3.5.1：
External Validation / 3.5.2：
Stability-Based Techniques / 3.5.3：
Dimension Reduction / 3.6：
Feature Transformation / 4.1：
Principal Component Analysis / 4.2.1：
Linear Discriminant Analysis / 4.2.2：
Feature Selection / 4.3：
Feature Selection in Supervised Learning / 4.3.1：
Unsupervised Feature Selection / 4.3.2：
Conclusions / 4.4：
Multimedia Applications / Part II：
Online Content-Based Image Retrieval Using Active Learning / Philippe-Henri Gosselin5：
Database Representation: Features and Similarity / 5.1：
Visual Features / 5.2.1：
Signature Based on Visual Pattern Dictionary / 5.2.2：
Similarity / 5.2.3：
Kernel Framework / 5.2.4：
Experiments / 5.2.5：
Classification Framework for Image Collection / 5.3：
Classification Methods for CBIR / 5.3.1：
Query Updating Scheme / 5.3.2：
Active Learning for CBIR / 5.3.3：
Notations for Selective Sampling Optimization / 5.4.1：
Active Learning Methods / 5.4.2：
Further Insights on Active Learning for CBIR / 5.5：
Active Boundary Correction / 5.5.1：
MAP vs Classification Error / 5.5.2：
Batch Selection / 5.5.3：
CBIR Interface: Result Display and Interaction / 5.5.4：
Conservative Learning for Object Detectors / Peter M. Roth ; Horst Bischof6：
Online Conservative Learning / 6.1：
Motion Detection / 6.2.1：
Reconstructive Model / 6.2.2：
Online AdaBoost for Feature Selection / 6.2.3：
Conservative Update Rules / 6.2.4：
Experimental Results / 6.3：
Description of Experiments / 6.3.1：
CoffeeCam / 6.3.2：
Switch to Caviar / 6.3.3：
Further Detection Results / 6.3.4：
Summary and Conclusions / 6.4：
Machine Learning Techniques for Face Analysis / Roberto Valenti ; Nicu Sebe ; Theo Gevers ; Ira Cohen7：
Background / 7.1：
Face Detection / 7.2.1：
Facial Feature Detection / 7.2.2：
Emotion Recognition Research / 7.2.3：
Learning Classifiers for Human-Computer Interaction / 7.3：
Model Is Correct / 7.3.1：
Model Is Incorrect / 7.3.2：
Discussion / 7.3.3：
Learning the Structure of Bayesian Network Classifiers / 7.4：
Bayesian Networks / 7.4.1：
Switching Between Simple Models / 7.4.2：
Beyond Simple Models / 7.4.3：
Classification-Driven Stochastic Structure Search / 7.4.4：
Should Unlabeled Be Weighed Differently? / 7.4.5：
Active Learning / 7.4.6：
Face Detection Experiments / 7.4.7：
Facial Expression Recognition Experiments / 7.5.2：
Mental Search in Image Databases: Implicit Versus Explicit Content Query / Julien Fauqueur ; Nozha Boujemaa7.6：
"Mental Image Search" Versus Other Search Paradigms / 8.1：
Implicit Content Query: Mental Image Search Using Bayesian Inference / 8.3：
Bayesian Inference for CBIR / 8.3.1：
Mental Image Category Search / 8.3.2：
Evaluation / 8.3.3：
Remarks / 8.3.4：
Explicit Content Query: Mental Image Search by Visual Composition Formulation / 8.4：
System Summary / 8.4.1：
Visual Thesaurus Construction / 8.4.2：
Symbolic Indexing, Boolean Search and Range Query Mechanism / 8.4.3：
Results / 8.4.4：
Combining Textual and Visual Information for Semantic Labeling of Images and Videos / Pinar Duygulu ; Muhammet Başstan ; Derya Ozkan8.4.5：
Semantic Labeling of Images / 9.1：
Translation Approach / 9.3：
Learning Correspondences Between Words and Regions / 9.3.1：
Linking Visual Elements to Words in News Videos / 9.3.2：
Translation Approach to Solve Video Association Problem / 9.3.3：
Experiments on News Videos Data Set / 9.3.4：
Naming Faces in News / 9.4：
Integrating Names and Faces / 9.4.1：
Finding Similarity of Faces / 9.4.2：
Finding the Densest Component in the Similarity Graph / 9.4.3：
Conclusions and Discussion / 9.4.4：
Machine Learning for Semi-structured Multimedia Documents: Application to Pornographic Filtering and Thematic Categorization. / Ludovic Denoyer ; Patrick Gallinari10：
Previous Work / 10.1：
Structured Document Classification / 10.2.1：
Multimedia Documents / 10.2.2：
Multimedia Generative Model / 10.3：
Classification of Documents / 10.3.1：
Generative Model / 10.3.2：
Description / 10.3.3：
Learning the Meta Model / 10.4：
Maximization of Lstructure / 10.4.1：
Maximization of Lcontent / 10.4.2：
Local Generative Models for Text and Image / 10.5：
Modelling a Piece of Text with Naive Bayes / 10.5.1：
Image Model / 10.5.2：
Models and Evaluation / 10.6：
Corpora / 10.6.2：
Results over the Pornographic Corpus / 10.6.3：
Results over the Wikipedia Multimedia Categorization Corpus / 10.6.4：
Conclusion / 10.7：
Classification and Clustering of Music for Novel Music Access Applications / Thomas Lidy ; Andreas Rauber11：
Feature Extraction from Audio / 11.1：
Low-Level Audio Features / 11.2.1：
MPEG-7 Audio Descriptors / 11.2.2：
MFCCs / 11.2.3：
MARSYAS Features / 11.2.4：
Rhythm Patterns / 11.2.5：
Statistical Spectrum Descriptors / 11.2.6：
Rhythm Histograms / 11.2.7：
Automatic Classifications of Music into Genres / 11.3：
Evaluation Through Music Classification / 11.3.1：
Benchmark Data Sets for Music Classification / 11.3.2：
Creating and Visualizing Music Maps Based on Self-organizing Maps / 11.4：
Class Visualization / 11.4.1：
Hit Histograms / 11.4.2：
U-Matrix / 11.4.3：
P-Matrix / 11.4.4：
U*-matrix / 11.4.5：
Gradient Fields / 11.4.6：
Component Planes / 11.4.7：
Smoothed Data Histograms / 11.4.8：
PlaySOM - Interaction with Music Maps / 11.5：
Interface / 11.5.1：
Interaction / 11.5.2：
Playlist Creation / 11.5.3：
PocketSOMPlayer - Music Retrieval on Mobile Devices / 11.6：
Playing Scenarios / 11.6.1：
Index / 11.6.3：
Introduction to Learning Principles for Multimedia Data / Part I：
Introduction to Bayesian Methods and Decision Theory / Simon P. Wilson ; Rozenn Dahyot ; Padraig Cunningham1：
Introduction / 1.1：
16.

電子ブック
ＥＢ
16. Machine Learning Techniques for Multimedia
D. M. Gabbay, Matthieu Cord, J. Siekmann, Pádraig Cunningham
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2008
所蔵情報: loading…
目次情報: 続きを見る
Introduction to Learning Principles for Multimedia Data / Part I：
Introduction to Bayesian Methods and Decision Theory / Simon P. Wilson ; Rozenn Dahyot ; Padraig Cunningham1：
Introduction / 1.1：
Uncertainty and Probability / 1.2：
Quantifying Uncertainty / 1.2.1：
The Laws of Probability / 1.2.2：
Interpreting Probability / 1.2.3：
The Partition Law and Bayes' Law / 1.2.4：
Probability Models, Parameters and Likelihoods / 1.3：
Bayesian Statistical Learning / 1.4：
Implementing Bayesian Statistical Learning Methods / 1.5：
Direct Simulation Methods / 1.5.1：
Markov Chain Monte Carlo / 1.5.2：
Monte Carlo Integration / 1.5.3：
Optimization Methods / 1.5.4：
Decision Theory / 1.6：
Utility and Choosing the Optimal Decision / 1.6.1：
Where Is the Utility? / 1.6.2：
Native Bayes / 1.7：
Further Reading / 1.8：
References
Supervised Learning / Matthieu Cord ; Sarah Jane Delany2：
Introduction to Statistical Learning / 2.1：
Risk Minimization / 2.2.1：
Empirical Risk Minimization / 2.2.2：
Risk Bounds / 2.2.3：
Support Vector Machines and Kernels / 2.3：
Linear Classification: SVM Principle / 2.3.1：
Soft Margin / 2.3.2：
Kernel-Based Classification / 2.3.3：
Nearest Neighbour Classification / 2.4：
Similarity and Distance Metrics / 2.4.1：
Other Distance Metrics for Multimedia Data / 2.4.2：
Computational Complexity / 2.4.3：
Instance Selection and Noise Reduction / 2.4.4：
k-NN: Advantages and Disadvantages / 2.4.5：
Ensemble Techniques / 2.5：
Bias-Variance Analysis of Error / 2.5.1：
Bagging / 2.5.3：
Random Forests / 2.5.4：
Boosting / 2.5.5：
Summary / 2.6：
Unsupervised Learning and Clustering / Derek Greene ; Páadraig Cunningham ; Rudolf Mayer3：
Basic Clustering Techniques / 3.1：
k-Means Clustering / 3.2.1：
Fuzzy Clustering / 3.2.2：
Hierarchical Clustering / 3.2.3：
Modern Clustering Techniques / 3.3：
Kernel Clustering / 3.3.1：
Spectral Clustering / 3.3.2：
Self-organizing Maps / 3.4：
SOM Architecture / 3.4.1：
SOM Algorithm / 3.4.2：
Self-organizing Map and Clustering / 3.4.3：
Variations of the Self-organizing Map / 3.4.4：
Cluster Validation / 3.5：
Internal Validation / 3.5.1：
External Validation / 3.5.2：
Stability-Based Techniques / 3.5.3：
Dimension Reduction / 3.6：
Feature Transformation / 4.1：
Principal Component Analysis / 4.2.1：
Linear Discriminant Analysis / 4.2.2：
Feature Selection / 4.3：
Feature Selection in Supervised Learning / 4.3.1：
Unsupervised Feature Selection / 4.3.2：
Conclusions / 4.4：
Multimedia Applications / Part II：
Online Content-Based Image Retrieval Using Active Learning / Philippe-Henri Gosselin5：
Database Representation: Features and Similarity / 5.1：
Visual Features / 5.2.1：
Signature Based on Visual Pattern Dictionary / 5.2.2：
Similarity / 5.2.3：
Kernel Framework / 5.2.4：
Experiments / 5.2.5：
Classification Framework for Image Collection / 5.3：
Classification Methods for CBIR / 5.3.1：
Query Updating Scheme / 5.3.2：
Active Learning for CBIR / 5.3.3：
Notations for Selective Sampling Optimization / 5.4.1：
Active Learning Methods / 5.4.2：
Further Insights on Active Learning for CBIR / 5.5：
Active Boundary Correction / 5.5.1：
MAP vs Classification Error / 5.5.2：
Batch Selection / 5.5.3：
CBIR Interface: Result Display and Interaction / 5.5.4：
Conservative Learning for Object Detectors / Peter M. Roth ; Horst Bischof6：
Online Conservative Learning / 6.1：
Motion Detection / 6.2.1：
Reconstructive Model / 6.2.2：
Online AdaBoost for Feature Selection / 6.2.3：
Conservative Update Rules / 6.2.4：
Experimental Results / 6.3：
Description of Experiments / 6.3.1：
CoffeeCam / 6.3.2：
Switch to Caviar / 6.3.3：
Further Detection Results / 6.3.4：
Summary and Conclusions / 6.4：
Machine Learning Techniques for Face Analysis / Roberto Valenti ; Nicu Sebe ; Theo Gevers ; Ira Cohen7：
Background / 7.1：
Face Detection / 7.2.1：
Facial Feature Detection / 7.2.2：
Emotion Recognition Research / 7.2.3：
Learning Classifiers for Human-Computer Interaction / 7.3：
Model Is Correct / 7.3.1：
Model Is Incorrect / 7.3.2：
Discussion / 7.3.3：
Learning the Structure of Bayesian Network Classifiers / 7.4：
Bayesian Networks / 7.4.1：
Switching Between Simple Models / 7.4.2：
Beyond Simple Models / 7.4.3：
Classification-Driven Stochastic Structure Search / 7.4.4：
Should Unlabeled Be Weighed Differently? / 7.4.5：
Active Learning / 7.4.6：
Face Detection Experiments / 7.4.7：
Facial Expression Recognition Experiments / 7.5.2：
Mental Search in Image Databases: Implicit Versus Explicit Content Query / Julien Fauqueur ; Nozha Boujemaa7.6：
"Mental Image Search" Versus Other Search Paradigms / 8.1：
Implicit Content Query: Mental Image Search Using Bayesian Inference / 8.3：
Bayesian Inference for CBIR / 8.3.1：
Mental Image Category Search / 8.3.2：
Evaluation / 8.3.3：
Remarks / 8.3.4：
Explicit Content Query: Mental Image Search by Visual Composition Formulation / 8.4：
System Summary / 8.4.1：
Visual Thesaurus Construction / 8.4.2：
Symbolic Indexing, Boolean Search and Range Query Mechanism / 8.4.3：
Results / 8.4.4：
Combining Textual and Visual Information for Semantic Labeling of Images and Videos / Pinar Duygulu ; Muhammet Başstan ; Derya Ozkan8.4.5：
Semantic Labeling of Images / 9.1：
Translation Approach / 9.3：
Learning Correspondences Between Words and Regions / 9.3.1：
Linking Visual Elements to Words in News Videos / 9.3.2：
Translation Approach to Solve Video Association Problem / 9.3.3：
Experiments on News Videos Data Set / 9.3.4：
Naming Faces in News / 9.4：
Integrating Names and Faces / 9.4.1：
Finding Similarity of Faces / 9.4.2：
Finding the Densest Component in the Similarity Graph / 9.4.3：
Conclusions and Discussion / 9.4.4：
Machine Learning for Semi-structured Multimedia Documents: Application to Pornographic Filtering and Thematic Categorization. / Ludovic Denoyer ; Patrick Gallinari10：
Previous Work / 10.1：
Structured Document Classification / 10.2.1：
Multimedia Documents / 10.2.2：
Multimedia Generative Model / 10.3：
Classification of Documents / 10.3.1：
Generative Model / 10.3.2：
Description / 10.3.3：
Learning the Meta Model / 10.4：
Maximization of Lstructure / 10.4.1：
Maximization of Lcontent / 10.4.2：
Local Generative Models for Text and Image / 10.5：
Modelling a Piece of Text with Naive Bayes / 10.5.1：
Image Model / 10.5.2：
Models and Evaluation / 10.6：
Corpora / 10.6.2：
Results over the Pornographic Corpus / 10.6.3：
Results over the Wikipedia Multimedia Categorization Corpus / 10.6.4：
Conclusion / 10.7：
Classification and Clustering of Music for Novel Music Access Applications / Thomas Lidy ; Andreas Rauber11：
Feature Extraction from Audio / 11.1：
Low-Level Audio Features / 11.2.1：
MPEG-7 Audio Descriptors / 11.2.2：
MFCCs / 11.2.3：
MARSYAS Features / 11.2.4：
Rhythm Patterns / 11.2.5：
Statistical Spectrum Descriptors / 11.2.6：
Rhythm Histograms / 11.2.7：
Automatic Classifications of Music into Genres / 11.3：
Evaluation Through Music Classification / 11.3.1：
Benchmark Data Sets for Music Classification / 11.3.2：
Creating and Visualizing Music Maps Based on Self-organizing Maps / 11.4：
Class Visualization / 11.4.1：
Hit Histograms / 11.4.2：
U-Matrix / 11.4.3：
P-Matrix / 11.4.4：
U*-matrix / 11.4.5：
Gradient Fields / 11.4.6：
Component Planes / 11.4.7：
Smoothed Data Histograms / 11.4.8：
PlaySOM - Interaction with Music Maps / 11.5：
Interface / 11.5.1：
Interaction / 11.5.2：
Playlist Creation / 11.5.3：
PocketSOMPlayer - Music Retrieval on Mobile Devices / 11.6：
Playing Scenarios / 11.6.1：
Index / 11.6.3：
Introduction to Learning Principles for Multimedia Data / Part I：
Introduction to Bayesian Methods and Decision Theory / Simon P. Wilson ; Rozenn Dahyot ; Padraig Cunningham1：
Introduction / 1.1：
17.

電子ブック
ＥＢ
17. Logical Tools for Handling Change in Agent-Based Systems
Dov M. Gabbay, Karl Schlechta
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2010
所蔵情報: loading…
目次情報: 続きを見る
Introduction and Motivation / 1：
Programme / 1.1：
Short Overview of the Different Logics / 1.2：
Nonmonotonic Logics / 1.2.1：
Theory Revision / 1.2.2：
Theory Update / 1.2.3：
Deontic Logic / 1.2.4：
Counterfactual Conditionals / 1.2.5：
Modal Logic / 1.2.6：
Intuitionistic Logic / 1.2.7：
Inheritance Systems / 1.2.8：
A Summarizing Table for the Semantics / 1.2.9：
A Discussion of Concepts / 1.3：
Basic Semantic Entities, Truth Values, and Operators / 1.3.1：
Algebraic and Structural Semantics / 1.3.2：
Restricted Operators and Relations / 1.3.3：
Copies in Preferential Models / 1.3.4：
Further Remarks on Universality of Representation Proofs / 1.3.5：
$$$ in the Object Language? / 1.3.6：
Various Considerations on Abstract Semantics / 1.3.7：
A Comparison with Reiter Defaults / 1.3.8：
IBRS / 1.4：
Definition and Comments / 1.4.1：
The Power of IBRS / 1.4.2：
Abstract Semantics for IBRS and Its Engineering Realization / 1.4.3：
Basic Definitions and Results / 2：
Algebraic Definitions / 2.1：
Basic Logical Definitions / 2.2：
Basic Definitions and Results for Nonmonotonic Logics / 2.3：
Abstract Semantics by Size / 3：
The First-Order Setting / 3.1：
General Size Semantics / 3.2：
Introduction / 3.2.1：
Main Table / 3.2.2：
Coherent Systems / 3.2.3：
Size and Principal Filter Logic / 3.2.4：
Preferential Structures - Part I / 4：
Remarks on Nonmonotonic Logics and Preferential Semantics / 4.1：
Basic Definitions / 4.1.2：
Preferential Structures Without Domain Conditions / 4.2：
General Discussion / 4.2.1：
Detailed Discussion / 4.2.2：
Preferential Structures - Part II / 5：
Simplifications by Domain Conditions, Logical Properties / 5.1：
Smooth Structures / 5.1.1：
Ranked Structures / 5.1.3：
The Logical Properties with Definability Preservation / 5.1.4：
A-Ranked Structures / 5.2：
Representation Results for A-Ranked Structures / 5.2.1：
Two-Sequent Calculi / 5.3：
Plausibility Logic / 5.3.1：
A Comment on the Work by Arieli and Avron / 5.3.3：
Blurred Observation - Absence of Definability Preservation / 5.4：
General and Smooth Structures Without Definability Preservation / 5.4.1：
The Limit Variant / 5.4.3：
The Algebraic Limit / 5.5.1：
The Logical Limit / 5.5.3：
Higher Preferential Structures / 6：
The General Case / 6.1：
Discussion of the Totally Smooth Case / 6.3：
The Essentially Smooth Case / 6.4：
Translation to Logic / 6.5：
Deontic Logic and Hierarchical Conditionals / 7：
Semantics of Deontic Logic / 7.1：
Introductory Remarks / 7.1.1：
Philosophical Discussion of Obligations / 7.1.2：
Examination of the Various Cases / 7.1.4：
What Is An Obligation? / 7.1.5：
Conclusion / 7.1.6：
A Comment on Work by Aqvist / 7.2：
There Are (At Least) Two Solutions / 7.2.1：
Outline / 7.2.3：
Gm $$$ A Implies G $$$ A (Outline) / 7.2.4：
Hierarchical Conditionals / 7.3：
Formal Modelling and Summary of Results / 7.3.1：
Overview / 7.3.3：
Connections with Other Concepts / 7.3.4：
Formal Results and Representation for Hierarchical Conditionals / 7.3.5：
Theory Update and Theory Revision / 8：
Update / 8.1：
Hidden Dimensions / 8.1.1：
Introduction to Theory Revision / 8.2：
Booth Revision / 8.2.2：
Revision and Independence / 8.2.3：
Preferential Modelling of Defaults / 8.2.4：
Remarks on Independence / 8.2.5：
An Analysis of Defeasible Inheritance Systems / 9：
Terminology / 9.1：
Inheritance and Reactive Diagrams / 9.1.2：
Conceptual Analysis / 9.1.3：
Introduction to Nonmonotonic Inheritance / 9.2：
Basic Discussion / 9.2.1：
Directly Sceptical Split Validity Upward Chaining Off-Path Inheritance / 9.2.2：
Review of Other Approaches and Problems / 9.2.3：
Defeasible Inheritance and Reactive Diagrams / 9.3：
Summary of Our Algorithm / 9.3.1：
Compilation and Memorization / 9.3.2：
Executing the Algorithm / 9.3.4：
Signposts / 9.3.5：
Beyond Inheritance / 9.3.6：
Interpretations / 9.4：
Informal Comparison of Inheritance with the Systems P and R / 9.4.1：
Inheritance as Information Transfer / 9.4.3：
Inheritance as Reasoning with Prototypes / 9.4.4：
Detailed Translation of Inheritance to Modified Systems of Small Sets / 9.5：
Normality / 9.5.1：
Small Sets / 9.5.2：
Bibliography
Index
Introduction and Motivation / 1：
Programme / 1.1：
Short Overview of the Different Logics / 1.2：
18.

電子ブック
ＥＢ
18. Logical Tools for Handling Change in Agent-Based Systems
Dov M. Gabbay, Karl Schlechta
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2010
所蔵情報: loading…
目次情報: 続きを見る
Introduction and Motivation / 1：
Programme / 1.1：
Short Overview of the Different Logics / 1.2：
Nonmonotonic Logics / 1.2.1：
Theory Revision / 1.2.2：
Theory Update / 1.2.3：
Deontic Logic / 1.2.4：
Counterfactual Conditionals / 1.2.5：
Modal Logic / 1.2.6：
Intuitionistic Logic / 1.2.7：
Inheritance Systems / 1.2.8：
A Summarizing Table for the Semantics / 1.2.9：
A Discussion of Concepts / 1.3：
Basic Semantic Entities, Truth Values, and Operators / 1.3.1：
Algebraic and Structural Semantics / 1.3.2：
Restricted Operators and Relations / 1.3.3：
Copies in Preferential Models / 1.3.4：
Further Remarks on Universality of Representation Proofs / 1.3.5：
$$$ in the Object Language? / 1.3.6：
Various Considerations on Abstract Semantics / 1.3.7：
A Comparison with Reiter Defaults / 1.3.8：
IBRS / 1.4：
Definition and Comments / 1.4.1：
The Power of IBRS / 1.4.2：
Abstract Semantics for IBRS and Its Engineering Realization / 1.4.3：
Basic Definitions and Results / 2：
Algebraic Definitions / 2.1：
Basic Logical Definitions / 2.2：
Basic Definitions and Results for Nonmonotonic Logics / 2.3：
Abstract Semantics by Size / 3：
The First-Order Setting / 3.1：
General Size Semantics / 3.2：
Introduction / 3.2.1：
Main Table / 3.2.2：
Coherent Systems / 3.2.3：
Size and Principal Filter Logic / 3.2.4：
Preferential Structures - Part I / 4：
Remarks on Nonmonotonic Logics and Preferential Semantics / 4.1：
Basic Definitions / 4.1.2：
Preferential Structures Without Domain Conditions / 4.2：
General Discussion / 4.2.1：
Detailed Discussion / 4.2.2：
Preferential Structures - Part II / 5：
Simplifications by Domain Conditions, Logical Properties / 5.1：
Smooth Structures / 5.1.1：
Ranked Structures / 5.1.3：
The Logical Properties with Definability Preservation / 5.1.4：
A-Ranked Structures / 5.2：
Representation Results for A-Ranked Structures / 5.2.1：
Two-Sequent Calculi / 5.3：
Plausibility Logic / 5.3.1：
A Comment on the Work by Arieli and Avron / 5.3.3：
Blurred Observation - Absence of Definability Preservation / 5.4：
General and Smooth Structures Without Definability Preservation / 5.4.1：
The Limit Variant / 5.4.3：
The Algebraic Limit / 5.5.1：
The Logical Limit / 5.5.3：
Higher Preferential Structures / 6：
The General Case / 6.1：
Discussion of the Totally Smooth Case / 6.3：
The Essentially Smooth Case / 6.4：
Translation to Logic / 6.5：
Deontic Logic and Hierarchical Conditionals / 7：
Semantics of Deontic Logic / 7.1：
Introductory Remarks / 7.1.1：
Philosophical Discussion of Obligations / 7.1.2：
Examination of the Various Cases / 7.1.4：
What Is An Obligation? / 7.1.5：
Conclusion / 7.1.6：
A Comment on Work by Aqvist / 7.2：
There Are (At Least) Two Solutions / 7.2.1：
Outline / 7.2.3：
Gm $$$ A Implies G $$$ A (Outline) / 7.2.4：
Hierarchical Conditionals / 7.3：
Formal Modelling and Summary of Results / 7.3.1：
Overview / 7.3.3：
Connections with Other Concepts / 7.3.4：
Formal Results and Representation for Hierarchical Conditionals / 7.3.5：
Theory Update and Theory Revision / 8：
Update / 8.1：
Hidden Dimensions / 8.1.1：
Introduction to Theory Revision / 8.2：
Booth Revision / 8.2.2：
Revision and Independence / 8.2.3：
Preferential Modelling of Defaults / 8.2.4：
Remarks on Independence / 8.2.5：
An Analysis of Defeasible Inheritance Systems / 9：
Terminology / 9.1：
Inheritance and Reactive Diagrams / 9.1.2：
Conceptual Analysis / 9.1.3：
Introduction to Nonmonotonic Inheritance / 9.2：
Basic Discussion / 9.2.1：
Directly Sceptical Split Validity Upward Chaining Off-Path Inheritance / 9.2.2：
Review of Other Approaches and Problems / 9.2.3：
Defeasible Inheritance and Reactive Diagrams / 9.3：
Summary of Our Algorithm / 9.3.1：
Compilation and Memorization / 9.3.2：
Executing the Algorithm / 9.3.4：
Signposts / 9.3.5：
Beyond Inheritance / 9.3.6：
Interpretations / 9.4：
Informal Comparison of Inheritance with the Systems P and R / 9.4.1：
Inheritance as Information Transfer / 9.4.3：
Inheritance as Reasoning with Prototypes / 9.4.4：
Detailed Translation of Inheritance to Modified Systems of Small Sets / 9.5：
Normality / 9.5.1：
Small Sets / 9.5.2：
Bibliography
Index
Introduction and Motivation / 1：
Programme / 1.1：
Short Overview of the Different Logics / 1.2：
19.

電子ブック
ＥＢ
19. Dependency Structures and Lexicalized Grammars
Marco Kuhlmann, Takeo Kanade
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2010
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1：
Motivation / 1.1：
Dependency Structures / 1.1.1：
Generative Capacity and Non-projectivity / 1.1.2：
Lexicalized Grammars Induce Dependency Trees / 1.2：
Overview of the Book / 1.3：
Dependency Languages / 1.3.1：
Contributions / 1.3.3：
Preliminaries / 2：
Projective Dependency Structures / 3：
Projectivity / 3.1：
Projectivity in the Sense of Harper and Hays / 3.1.1：
Projectivity in the Sense of Lecerf and Ihm / 3.1.2：
Projectivity in the Sense of Fitialov / 3.1.3：
Related Work / 3.1.4：
Algebraic Framework / 3.2：
Tree Traversal Strategies / 3.2.1：
Traversal of Treelet-Ordered Trees / 3.2.2：
Order Annotations / 3.2.3：
Dependency Algebras / 3.2.4：
Algorithmic Problems / 3.3：
Encoding and Decoding / 3.3.1：
Testing whether a Dependency Structure Is Projective / 3.3.2：
Empirical Evaluation / 3.3.3：
The Projectivity Hypothesis / 3.4.1：
Experimental Setup / 3.4.2：
Results and Discussion / 3.4.3：
Dependency Structures of Bounded Degree / 3.4.4：
The Block-Degree Measure / 4.1：
Blocks and Block-Degree / 4.1.1：
A Hierarchy of Non-projective Dependency Structures / 4.1.2：
Traversal of Block-Ordered Trees / 4.1.3：
Segmented Dependency Structures / 4.2.2：
Dependency Structure Algebras / 4.2.3：
Encoding / 4.3：
Computing the Block-Degree of a Dependency Structure / 4.3.2：
Dependency Structures without Crossings / 4.4：
Weakly Non-projective Dependency Structures / 5.1：
Definition of Weak Non-projectivity / 5.1.1：
Relation to the Block-Degree Measure / 5.1.2：
Algebraic Opaqueness / 5.1.3：
Well-Nested Dependency Structures / 5.1.4：
Definition of Well-Nestedness / 5.2.1：
Non-crossing Partitions / 5.2.2：
Algebraic Characterization / 5.2.3：
Testing whether a Dependency Structure Is Well-Nested / 5.2.4：
Structures and Grammars / 5.2.5：
Context-Free Grammars / 6.1：
Definition / 6.1.1：
String Semantics / 6.1.2：
Linearization Semantics / 6.1.3：
Dependency Semantics / 6.1.4：
Linear Context-Free Rewriting Systems / 6.2：
Non-essential Concatenation Functions / 6.2.1：
Coupled Context-Free Grammars / 6.2.4：
Tree Adjoining Grammar / 6.3.1：
Regular Dependency Languages / 6.4.1：
Regular Sets of Dependency Structures / 7.1：
Algebraic Recognizability / 7.1.1：
Elementary Properties / 7.1.2：
Regular Term Grammars / 7.1.3：
Regular Dependency Grammars / 7.1.4：
Dependency Languages and Lexicalized Grammars / 7.1.5：
Pumping Lemmata / 7.2：
The Pumping Lemma for Regular Term Languages / 7.2.1：
Ogden's Lemma for Regular Term Languages / 7.2.2：
Constant Growth / 7.3：
Constant Growth and Semilinearity / 7.3.1：
Regular Term Languages are Semilinear / 7.3.2：
Generative Capacity and Parsing Complexity / 7.3.3：
Projection of String Languages / 8.1：
Labelled Dependency Structures / 8.1.1：
String-Generating Regular Dependency Grammars / 8.1.2：
String-Generative Capacity / 8.1.3：
String Languages and Structural Properties / 8.2：
Masked Strings / 8.2.1：
Enforcing a Given Block-Degree / 8.2.2：
Enforcing Ill-Nestedness / 8.2.3：
Hierarchies of String Languages / 8.2.4：
Parsing Complexity / 8.2.5：
Membership Problems / 8.3.1：
The Standard Membership Problem / 8.3.2：
The Uniform Membership Problem / 8.3.3：
Recognition of Well-Nested Languages / 8.3.4：
Conclusion / 8.3.5：
Main Contributions / 9.1：
Future Directions / 9.2：
Development of the Formalism / 9.2.1：
Linguistic Relevance / 9.2.2：
Applications to Parsing / 9.2.3：
An Algebraic Perspective on Grammar Formalisms / 9.2.4：
References
Index
Introduction / 1：
Motivation / 1.1：
Dependency Structures / 1.1.1：
20.

電子ブック
ＥＢ
20. Adaptive Bidding in Single-Sided Auctions Under Uncertainty
Clemens van Dinther, Stefan Brantschen, Clemens van Dinther, Marius Walliser
出版情報: Springer eBooks Computer Science , Birkh?user Basel, 2007
所蔵情報: loading…
目次情報: 続きを見る
List of Figures
List of Tables
Motivation and Fundamentals / I：
Introduction / 1：
Problem Description and Research Questions / 1.1：
Organization of the Book / 1.2：
Economic Foundations / 2：
Electronic Markets and Strategic Bidding / 2.1：
Historical Background of Markets / 2.1.1：
Markets as an Economic System / 2.1.2：
Market Institution Types and Auctions / 2.1.3：
Bidding in Auctions under Uncertainty / 2.1.4：
Market Engineering / 2.2：
Structured Design Approach / 2.2.1：
Generic Design Approaches / 2.2.2：
Computer Aided Market Engineering / 2.3：
Conceptual Platform Design / 2.3.1：
The meet2trade Software Suite / 2.3.2：
Summary / 2.4：
Agent-based Computational Economics / 3：
Intelligent Software Agents / 3.1：
Characteristics of Software Agents / 3.1.1：
Agent Learning / 3.1.2：
Agent Architecture / 3.1.3：
Multi Agent Systems / 3.2：
Communication and Interaction in Multi-Agent Systems / 3.2.1：
Coordination in Multi Agent Systems / 3.2.2：
Building Multi Agent-based Simulation Models / 3.3：
Characteristics of Simulations / 3.3.1：
Developing and Applying Simulations / 3.3.2：
Agent-based Simulation Approaches and Tools / 3.4：
Methodological Approaches of MABS in Economics / 4：
Pure Agent-based Simulation: The Bottom-up Approach / 4.1：
Monte Carlo Simulation / 4.2：
Evolutionary Approach / 4.3：
Reinforcement Learning / 4.4：
The Learning Model / 4.4.1：
Markov Games / 4.4.2：
Agent-based Simulation Software / 4.5：
Design Objectives for Agent-based Simulation Software / 5.1：
Functional and Technical Requirements / 5.1.1：
Agent-based Simulation Software - An Overview / 5.1.2：
Requirements Analysis of Agent Platforms / 5.1.3：
The Java Agent Development Framework / 5.2：
Agent Platform Architecture / 5.2.1：
Agent Model / 5.2.2：
Development Tools / 5.2.3：
The Agent-based Market Simulation Environment / 5.3：
Architecture / 5.3.1：
Simulation Control Agent (SCA) / 5.3.2：
Simulation Agents and Behaviours / 5.3.3：
Examples for Simulations on AMASE / 5.3.4：
Examination of Bidding under Uncertainty / 5.4：
Simulation Design / 6：
The Simulation Model / 6.1：
Bidding Process and Action Space / 6.1.1：
Agents and Environment / 6.1.2：
The Reinforcement Learning Mechanism / 6.1.3：
Simulation Approach / 6.2：
Initial Parameter Values of the Simulation / 6.2.1：
Simulation Settings / 6.2.2：
Theoretical Benchmark / 6.2.3：
Assessment of the Simulation Results / 7：
Institutional Rules: Sealed Bid versus Ascending Second Price Auction / 7.1：
Two Agent Environment / 7.1.1：
Five Agent Environment / 7.1.2：
Impact of Information Acquisition Cost / 7.2：
Concluding Discussion and Future Research / 7.2.1：
Conclusion / 8：
Summary of the Main Contributions / 8.1：
Limitations of the Approach / 8.2：
Outlook / 8.3：
Appendices and Bibliography
Appendix: Mathematical Proofs / A：
Expected Social Welfare / A.1：
Auctioneer's Expected Revenue / A.2：
Bidders' Expected Payoff / A.3：
Uncertain Bidders' Expected Payoff / A.4：
Density Function for the Estimated Valuations / A.5：
Appendix: Simulation Data and Figures / B：
Data / B.1：
Institutional Rules: Two Bidder / B.1.1：
Institutional Rules: Five Bidder / B.1.2：
Information Acquisition Costs: Two Bidder / B.1.3：
Information Acquisition Cost: Five Bidder / B.1.4：
Figures / B.2：
Bibliography
Abbreviations
List of Figures
List of Tables
Motivation and Fundamentals / I：
21.

電子ブック
ＥＢ
21. Dependency Structures and Lexicalized Grammars
Marco Kuhlmann, Takeo Kanade, Josef Kittler
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2010
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1：
Motivation / 1.1：
Dependency Structures / 1.1.1：
Generative Capacity and Non-projectivity / 1.1.2：
Lexicalized Grammars Induce Dependency Trees / 1.2：
Overview of the Book / 1.3：
Dependency Languages / 1.3.1：
Contributions / 1.3.3：
Preliminaries / 2：
Projective Dependency Structures / 3：
Projectivity / 3.1：
Projectivity in the Sense of Harper and Hays / 3.1.1：
Projectivity in the Sense of Lecerf and Ihm / 3.1.2：
Projectivity in the Sense of Fitialov / 3.1.3：
Related Work / 3.1.4：
Algebraic Framework / 3.2：
Tree Traversal Strategies / 3.2.1：
Traversal of Treelet-Ordered Trees / 3.2.2：
Order Annotations / 3.2.3：
Dependency Algebras / 3.2.4：
Algorithmic Problems / 3.3：
Encoding and Decoding / 3.3.1：
Testing whether a Dependency Structure Is Projective / 3.3.2：
Empirical Evaluation / 3.3.3：
The Projectivity Hypothesis / 3.4.1：
Experimental Setup / 3.4.2：
Results and Discussion / 3.4.3：
Dependency Structures of Bounded Degree / 3.4.4：
The Block-Degree Measure / 4.1：
Blocks and Block-Degree / 4.1.1：
A Hierarchy of Non-projective Dependency Structures / 4.1.2：
Traversal of Block-Ordered Trees / 4.1.3：
Segmented Dependency Structures / 4.2.2：
Dependency Structure Algebras / 4.2.3：
Encoding / 4.3：
Computing the Block-Degree of a Dependency Structure / 4.3.2：
Dependency Structures without Crossings / 4.4：
Weakly Non-projective Dependency Structures / 5.1：
Definition of Weak Non-projectivity / 5.1.1：
Relation to the Block-Degree Measure / 5.1.2：
Algebraic Opaqueness / 5.1.3：
Well-Nested Dependency Structures / 5.1.4：
Definition of Well-Nestedness / 5.2.1：
Non-crossing Partitions / 5.2.2：
Algebraic Characterization / 5.2.3：
Testing whether a Dependency Structure Is Well-Nested / 5.2.4：
Structures and Grammars / 5.2.5：
Context-Free Grammars / 6.1：
Definition / 6.1.1：
String Semantics / 6.1.2：
Linearization Semantics / 6.1.3：
Dependency Semantics / 6.1.4：
Linear Context-Free Rewriting Systems / 6.2：
Non-essential Concatenation Functions / 6.2.1：
Coupled Context-Free Grammars / 6.2.4：
Tree Adjoining Grammar / 6.3.1：
Regular Dependency Languages / 6.4.1：
Regular Sets of Dependency Structures / 7.1：
Algebraic Recognizability / 7.1.1：
Elementary Properties / 7.1.2：
Regular Term Grammars / 7.1.3：
Regular Dependency Grammars / 7.1.4：
Dependency Languages and Lexicalized Grammars / 7.1.5：
Pumping Lemmata / 7.2：
The Pumping Lemma for Regular Term Languages / 7.2.1：
Ogden's Lemma for Regular Term Languages / 7.2.2：
Constant Growth / 7.3：
Constant Growth and Semilinearity / 7.3.1：
Regular Term Languages are Semilinear / 7.3.2：
Generative Capacity and Parsing Complexity / 7.3.3：
Projection of String Languages / 8.1：
Labelled Dependency Structures / 8.1.1：
String-Generating Regular Dependency Grammars / 8.1.2：
String-Generative Capacity / 8.1.3：
String Languages and Structural Properties / 8.2：
Masked Strings / 8.2.1：
Enforcing a Given Block-Degree / 8.2.2：
Enforcing Ill-Nestedness / 8.2.3：
Hierarchies of String Languages / 8.2.4：
Parsing Complexity / 8.2.5：
Membership Problems / 8.3.1：
The Standard Membership Problem / 8.3.2：
The Uniform Membership Problem / 8.3.3：
Recognition of Well-Nested Languages / 8.3.4：
Conclusion / 8.3.5：
Main Contributions / 9.1：
Future Directions / 9.2：
Development of the Formalism / 9.2.1：
Linguistic Relevance / 9.2.2：
Applications to Parsing / 9.2.3：
An Algebraic Perspective on Grammar Formalisms / 9.2.4：
References
Index
Introduction / 1：
Motivation / 1.1：
Dependency Structures / 1.1.1：
22.

電子ブック
ＥＢ
22. Adaptive Bidding in Single-Sided Auctions Under Uncertainty
Clemens van Dinther, Stefan Brantschen, Clemens van Dinther, Marius Walliser, Monique Calisti
出版情報: SpringerLink Books - AutoHoldings , Birkhäuser Basel, 2007
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List of Figures
List of Tables
Motivation and Fundamentals / I：
Introduction / 1：
Problem Description and Research Questions / 1.1：
Organization of the Book / 1.2：
Economic Foundations / 2：
Electronic Markets and Strategic Bidding / 2.1：
Historical Background of Markets / 2.1.1：
Markets as an Economic System / 2.1.2：
Market Institution Types and Auctions / 2.1.3：
Bidding in Auctions under Uncertainty / 2.1.4：
Market Engineering / 2.2：
Structured Design Approach / 2.2.1：
Generic Design Approaches / 2.2.2：
Computer Aided Market Engineering / 2.3：
Conceptual Platform Design / 2.3.1：
The meet2trade Software Suite / 2.3.2：
Summary / 2.4：
Agent-based Computational Economics / 3：
Intelligent Software Agents / 3.1：
Characteristics of Software Agents / 3.1.1：
Agent Learning / 3.1.2：
Agent Architecture / 3.1.3：
Multi Agent Systems / 3.2：
Communication and Interaction in Multi-Agent Systems / 3.2.1：
Coordination in Multi Agent Systems / 3.2.2：
Building Multi Agent-based Simulation Models / 3.3：
Characteristics of Simulations / 3.3.1：
Developing and Applying Simulations / 3.3.2：
Agent-based Simulation Approaches and Tools / 3.4：
Methodological Approaches of MABS in Economics / 4：
Pure Agent-based Simulation: The Bottom-up Approach / 4.1：
Monte Carlo Simulation / 4.2：
Evolutionary Approach / 4.3：
Reinforcement Learning / 4.4：
The Learning Model / 4.4.1：
Markov Games / 4.4.2：
Agent-based Simulation Software / 4.5：
Design Objectives for Agent-based Simulation Software / 5.1：
Functional and Technical Requirements / 5.1.1：
Agent-based Simulation Software - An Overview / 5.1.2：
Requirements Analysis of Agent Platforms / 5.1.3：
The Java Agent Development Framework / 5.2：
Agent Platform Architecture / 5.2.1：
Agent Model / 5.2.2：
Development Tools / 5.2.3：
The Agent-based Market Simulation Environment / 5.3：
Architecture / 5.3.1：
Simulation Control Agent (SCA) / 5.3.2：
Simulation Agents and Behaviours / 5.3.3：
Examples for Simulations on AMASE / 5.3.4：
Examination of Bidding under Uncertainty / 5.4：
Simulation Design / 6：
The Simulation Model / 6.1：
Bidding Process and Action Space / 6.1.1：
Agents and Environment / 6.1.2：
The Reinforcement Learning Mechanism / 6.1.3：
Simulation Approach / 6.2：
Initial Parameter Values of the Simulation / 6.2.1：
Simulation Settings / 6.2.2：
Theoretical Benchmark / 6.2.3：
Assessment of the Simulation Results / 7：
Institutional Rules: Sealed Bid versus Ascending Second Price Auction / 7.1：
Two Agent Environment / 7.1.1：
Five Agent Environment / 7.1.2：
Impact of Information Acquisition Cost / 7.2：
Concluding Discussion and Future Research / 7.2.1：
Conclusion / 8：
Summary of the Main Contributions / 8.1：
Limitations of the Approach / 8.2：
Outlook / 8.3：
Appendices and Bibliography
Appendix: Mathematical Proofs / A：
Expected Social Welfare / A.1：
Auctioneer's Expected Revenue / A.2：
Bidders' Expected Payoff / A.3：
Uncertain Bidders' Expected Payoff / A.4：
Density Function for the Estimated Valuations / A.5：
Appendix: Simulation Data and Figures / B：
Data / B.1：
Institutional Rules: Two Bidder / B.1.1：
Institutional Rules: Five Bidder / B.1.2：
Information Acquisition Costs: Two Bidder / B.1.3：
Information Acquisition Cost: Five Bidder / B.1.4：
Figures / B.2：
Bibliography
Abbreviations
List of Figures
List of Tables
Motivation and Fundamentals / I：
23.

電子ブック
ＥＢ
23. An Introduction to Language Processing with Perl and Prolog
Pierre M. Nugues
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2006
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An Overview of Language Processing / 1：
Linguistics and Language Processing / 1.1：
Applications of Language Processing / 1.2：
The Different Domains of Language Processing / 1.3：
Phonetics / 1.4：
Lexicon and Morphology / 1.5：
Syntax / 1.6：
Syntax as Defined by Noam Chomsky / 1.6.1：
Syntax as Relations and Dependencies / 1.6.2：
Semantics / 1.7：
Discourse and Dialogue / 1.8：
Why Speech and Language Processing Are Difficult / 1.9：
Ambiguity / 1.9.1：
Models and Their Implementation / 1.9.2：
An Example of Language Technology in Action: the Persona Project / 1.10：
Overview of Persona / 1.10.1：
The Persona's Modules / 1.10.2：
Further Reading / 1.11：
Corpus Processing Tools / 2：
Corpora / 2.1：
Types of Corpora / 2.1.1：
Corpora and Lexicon Building / 2.1.2：
Corpora as Knowledge Sources for the Linguist / 2.1.3：
Finite-State Automata / 2.2：
A Description / 2.2.1：
Mathematical Definition of Finite-State Automata / 2.2.2：
Finite-State Automata in Prolog / 2.2.3：
Deterministic and Nondeterministic Automata / 2.2.4：
Building a Deterministic Automata from a Nondeterministic One / 2.2.5：
Searching a String with a Finite-State Automaton / 2.2.6：
Operations on Finite-State Automata / 2.2.7：
Regular Expressions / 2.3：
Repetition Metacharacters / 2.3.1：
The Longest Match / 2.3.2：
Character Classes / 2.3.3：
Nonprintable Symbols or Positions / 2.3.4：
Union and Boolean Operators / 2.3.5：
Operator Combination and Precedence / 2.3.6：
Programming with Regular Expressions / 2.4：
Perl / 2.4.1：
Matching / 2.4.2：
Substitutions / 2.4.3：
Translating Characters / 2.4.4：
String Operators / 2.4.5：
Back References / 2.4.6：
Finding Concordances / 2.5：
Concordances in Prolog / 2.5.1：
Concordances in Perl / 2.5.2：
Approximate String Matching / 2.6：
Edit Operations / 2.6.1：
Minimum Edit Distance / 2.6.2：
Searching Edits in Prolog / 2.6.3：
Encoding, Entropy, and Annotation Schemes / 2.7：
Encoding Texts / 3.1：
Character Sets / 3.2：
Representing Characters / 3.2.1：
Unicode / 3.2.2：
The Unicode Encoding Schemes / 3.2.3：
Locales and Word Order / 3.3：
Presenting Time, Numerical Information, and Ordered Words / 3.3.1：
The Unicode Collation Algorithm / 3.3.2：
Markup Languages / 3.4：
A Brief Background / 3.4.1：
An Outline of XML / 3.4.2：
Writing a DTD / 3.4.3：
Writing an XML Document / 3.4.4：
Namespaces / 3.4.5：
Codes and Information Theory / 3.5：
Entropy / 3.5.1：
Huffman Encoding / 3.5.2：
Cross Entropy / 3.5.3：
Perplexity and Cross Perplexity / 3.5.4：
Entropy and Decision Trees / 3.6：
Decision Trees / 3.6.1：
Inducing Decision Trees Automatically / 3.6.2：
Counting Words / 3.7：
Counting Words and Word Sequences / 4.1：
Words and Tokens / 4.2：
What Is a Word? / 4.2.1：
Breaking a Text into Words: Tokenization / 4.2.2：
Tokenizing Texts / 4.3：
Tokenizing Texts in Prolog / 4.3.1：
Tokenizing Texts in Perl / 4.3.2：
N-grams / 4.4：
Some Definitions / 4.4.1：
Counting Unigrams in Prolog / 4.4.2：
Counting Unigrams with Perl / 4.4.3：
Counting Bigrams with Perl / 4.4.4：
Probabilistic Models of a Word Sequence / 4.5：
The Maximum Likelihood Estimation / 4.5.1：
Using ML Estimates with Nineteen Eighty-Four / 4.5.2：
Smoothing N-gram Probabilities / 4.6：
Sparse Data / 4.6.1：
Laplace's Rule / 4.6.2：
Good-Turing Estimation / 4.6.3：
Using N-grams of Variable Length / 4.7：
Linear Interpolation / 4.7.1：
Back-off / 4.7.2：
Quality of a Language Model / 4.8：
Intuitive Presentation / 4.8.1：
Entropy Rate / 4.8.2：
Perplexity / 4.8.3：
Collocations / 4.9：
Word Preference Measurements / 4.9.1：
Extracting Collocations with Perl / 4.9.2：
Application: Retrieval and Ranking of Documents on the Web / 4.10：
Words, Parts of Speech, and Morphology / 4.11：
Words / 5.1：
Parts of Speech / 5.1.1：
Features / 5.1.2：
Two Significant Parts of Speech: The Noun and the Verb / 5.1.3：
Lexicons / 5.2：
Encoding a Dictionary / 5.2.1：
Building a Trie in Prolog / 5.2.2：
Finding a Word in a Trie / 5.2.3：
Morphology / 5.3：
Morphemes / 5.3.1：
Morphs / 5.3.2：
Inflection and Derivation / 5.3.3：
Language Differences / 5.3.4：
Morphological Parsing / 5.4：
Two-Level Model of Morphology / 5.4.1：
Interpreting the Morphs / 5.4.2：
Finite-State Transducers / 5.4.3：
Conjugating a French Verb / 5.4.4：
Prolog Implementation / 5.4.5：
Operations on Finite-State Transducers / 5.4.6：
Morphological Rules / 5.5：
Two-Level Rules / 5.5.1：
Rules and Finite-State Transducers / 5.5.2：
Rule Composition: An Examplewith French Irregular Verbs / 5.5.3：
Application Examples / 5.6：
Part-of-Speech Tagging Using Rules / 5.7：
Resolving Part-of-Speech Ambiguity / 6.1：
A Manual Method / 6.1.1：
Which Method to Use to Automatically Assign Parts of Speech / 6.1.2：
Tagging with Rules / 6.2：
Brill's Tagger / 6.2.1：
Implementation in Prolog / 6.2.2：
Deriving Rules Automatically / 6.2.3：
Confusion Matrices / 6.2.4：
Unknown Words / 6.3：
Standardized Part-of-Speech Tagsets / 6.4：
Multilingual Part-of-Speech Tags / 6.4.1：
Parts of Speechfor English / 6.4.2：
An Annotation Schemefor Swedish / 6.4.3：
Part-of-Speech Tagging Using Stochastic Techniques / 6.5：
The Noisy Channel Model / 7.1：
Presentation / 7.1.1：
The N-gram Approximation / 7.1.2：
Tagging a Sentence / 7.1.3：
The Viterbi Algorithm: An Intuitive Presentation / 7.1.4：
Markov Models / 7.2：
Markov Chains / 7.2.1：
Hidden Markov Models / 7.2.2：
Three Fundamental Algorithms to Solve Problems with HMMs / 7.2.3：
The Forward Procedure / 7.2.4：
Viterbi Algorithm / 7.2.5：
The Backward Procedure / 7.2.6：
The Forward-Backward Algorithm / 7.2.7：
Tagging with Decision Trees / 7.3：
An Application of the Noisy Channel Model: Spell Checking / 7.4：
A Second Application: Language Models for Machine Translation / 7.6：
Parallel Corpora / 7.6.1：
Alignment / 7.6.2：
Translation / 7.6.3：
Phrase-Structure Grammars in Prolog / 7.7：
Using Prolog to Write Phrase-Structure Grammars / 8.1：
Representing Chomsky's Syntactic Formalism in Prolog / 8.2：
Constituents / 8.2.1：
Tree Structures / 8.2.2：
Phrase-Structure Rules / 8.2.3：
The Definite Clause Grammar (DCG) Notation / 8.2.4：
Parsing with DCGs / 8.3：
Translating DCGs into Prolog Clauses / 8.3.1：
Parsing and Generation / 8.3.2：
Left-Recursive Rules / 8.3.3：
Parsing Ambiguity / 8.4：
Using Variables / 8.5：
Gender and Number Agreement / 8.5.1：
Obtaining the Syntactic Structure / 8.5.2：
Application: Tokenizing Texts Using DCG Rules / 8.6：
Word Breaking / 8.6.1：
Recognition of Sentence Boundaries / 8.6.2：
Semantic Representation / 8.7：
A-Calculus / 8.7.1：
Embedding A-Expressions into DCG Rules / 8.7.2：
Semantic Composition of Verbs / 8.7.3：
An Application of Phrase-Structure Grammars and a Worked Example / 8.8：
Partial Parsing / 8.9：
Is Syntax Necessary? / 9.1：
Word Spotting and Template Matching / 9.2：
ELIZA / 9.2.1：
Word Spotting in Prolog / 9.2.2：
Multiword Detection / 9.3：
Multiwords / 9.3.1：
AStandard Multiword Annotation / 9.3.2：
Detecting Multiwords with Rules / 9.3.3：
Running the Program / 9.3.4：
Noun Groups and Verb Groups / 9.4：
Groups Versus Recursive Phrases / 9.4.1：
DCG Rules to Detect Noun Groups / 9.4.2：
DCG Rules to Detect Verb Groups / 9.4.3：
Running the Rules / 9.4.4：
Group Detection as a Tagging Problem / 9.5：
Tagging Gaps / 9.5.1：
Tagging Words / 9.5.2：
Using Symbolic Rules / 9.5.3：
Using Statistical Tagging / 9.5.4：
Cascading Partial Parsers / 9.6：
Elementary Analysis of Grammatical Functions / 9.7：
Main Functions / 9.7.1：
Extracting Other Groups / 9.7.2：
An Annotation Scheme for Groups in French / 9.8：
Application: The FASTUS System / 9.9：
The Message Understanding Conferences / 9.9.1：
The Syntactic Layers of the FASTUS System / 9.9.2：
Evaluationof Information Extraction Systems / 9.9.3：
Syntactic Formalisms / 9.10：
Introduction / 10.1：
Chomsky's Grammar in Syntactic Structures / 10.2：
Constituency: A Formal Definition / 10.2.1：
Transformations / 10.2.2：
Transformations and Movements / 10.2.3：
Gap Threading / 10.2.4：
Gap Threading to Parse Relative Clauses / 10.2.5：
Standardized Phrase Categories for English / 10.3：
Unification-Based Grammars / 10.4：
Representing Features in Prolog / 10.4.1：
A Formalism for Features and Rules / 10.4.3：
Features Organization / 10.4.4：
Features and Unification / 10.4.5：
A Unification Algorithm for Feature Structures / 10.4.6：
Dependency Grammars / 10.5：
Properties of a Dependency Graph / 10.5.1：
Valence / 10.5.3：
Dependencies and Functions / 10.5.4：
Parsing Techniques / 10.6：
Bottom-up Parsing / 11.1：
The Shift-Reduce Algorithm / 11.2.1：
Implementing Shift-Reduce Parsing in Prolog / 11.2.2：
Differences Between Bottom-up and Top-down Parsing / 11.2.3：
Chart Parsing / 11.3：
Backtracking and Efficiency / 11.3.1：
Structure of a Chart / 11.3.2：
The Active Chart / 11.3.3：
Modules of an Earley Parser / 11.3.4：
The Earley Algorithm in Prolog / 11.3.5：
The Earley Parser to Handle Left-Recursive Rules and Empty Symbols / 11.3.6：
Probabilistic Parsing of Context-Free Grammars / 11.4：
A Description of PCFGs / 11.5：
The Bottom-up Chart / 11.5.1：
The Cocke-Younger-Kasami Algorithm in Prolog / 11.5.2：
Adding Probabilities to the CYK Parser / 11.5.3：
Parser Evaluation / 11.6：
Constituency-Based Evaluation / 11.6.1：
Dependency-Based Evaluation / 11.6.2：
PerformanceofPCFG Parsing / 11.6.3：
Parsing Dependencies / 11.7：
Dependency Rules / 11.7.1：
Extending the Shift-Reduce Algorithm to Parse Dependencies / 11.7.2：
Nivre's Parser in Prolog / 11.7.3：
Finding Dependencies Using Constraints / 11.7.4：
Parsing Dependencies Using Statistical Techniques / 11.7.5：
Semantics and Predicate Logic / 11.8：
Language Meaning and Logic: An Illustrative Example / 12.1：
Formal Semantics / 12.3：
First-Order Predicate Calculus to Represent the State of Affairs / 12.4：
Variables and Constants / 12.4.1：
Predicates / 12.4.2：
Querying the Universe of Discourse / 12.5：
Mapping Phrases onto Logical Formulas / 12.6：
Representing Nouns and Adjectives / 12.6.1：
Representing Noun Groups / 12.6.2：
Representing Verbs and Prepositions / 12.6.3：
The Case of Determiners / 12.7：
Determiners and Logic Quantifiers / 12.7.1：
Translating Sentences Using Quantifiers / 12.7.2：
A General Representation of Sentences / 12.7.3：
Compositionality to Translate Phrases to Logical Forms / 12.8：
Translating the Noun Phrase / 12.8.1：
Translating the Verb Phrase / 12.8.2：
Augmenting the Database and Answering Questions / 12.9：
Declarations / 12.9.1：
Questions with Existential and Universal Quantifiers / 12.9.2：
Prolog and Unknown Predicates / 12.9.3：
Other Determiners and Questions / 12.9.4：
Application: The Spoken Language Translator / 12.10：
Translating Spoken Sentences / 12.10.1：
Compositional Semantics / 12.10.2：
Semantic Representation Transfer / 12.10.3：
Lexical Semantics / 12.11：
Beyond Formal Semantics / 13.1：
La langue etlaparole / 13.1.1：
Language and the Structure of the World / 13.1.2：
Lexical Structures / 13.2：
Some Basic Terms and Concepts / 13.2.1：
Ontological Organization / 13.2.2：
Lexical Classes and Relations / 13.2.3：
Semantic Networks / 13.2.4：
Building a Lexicon / 13.3：
The Lexicon and Word Senses / 13.3.1：
Verb Models / 13.3.2：
Definitions / 13.3.3：
An Example of Exhaustive Lexical Organization: Word Net / 13.4：
Nouns / 13.4.1：
Adjectives / 13.4.2：
Verbs / 13.4.3：
Automatic Word Sense Disambiguation / 13.5：
Senses as Tags / 13.5.1：
Associating a Word with a Context / 13.5.2：
Guessing the Topic / 13.5.3：
Naive Bayes / 13.5.4：
Using Constraints on Verbs / 13.5.5：
Using Dictionary Definitions / 13.5.6：
An Unsupervised Algorithm to Tag Senses / 13.5.7：
Senses and Languages / 13.5.8：
Case Grammars / 13.6：
Cases in Latin / 13.6.1：
Cases and Thematic Roles / 13.6.2：
Parsing with Cases / 13.6.3：
Semantic Grammars / 13.6.4：
Extending Case Grammars / 13.7：
Frame Net / 13.7.1：
A Statistical Method to Identify Semantic Roles / 13.7.2：
An Example of Case Grammar Application: EVAR / 13.8：
EVAR's Ontology and Syntactic Classes / 13.8.1：
Cases in EVAR / 13.8.2：
Discourse / 13.9：
Discourse: A Minimalist Definition / 14.1：
A Description of Discourse / 14.2.1：
Discourse Entities / 14.2.2：
References: An Application-Oriented View / 14.3：
References and Noun Phrases / 14.3.1：
Finding Names - Proper Nouns / 14.3.2：
Coreference / 14.4：
Anaphora / 14.4.1：
Solving Coreferences in an Example / 14.4.2：
A Standard Coreference Annotation / 14.4.3：
References: A More Formal View / 14.5：
Generating Discourse Entities: The Existential Quantifier / 14.5.1：
Retrieving Discourse Entities: Definite Descriptions / 14.5.2：
Generating Discourse Entities: The Universal Quantifier / 14.5.3：
Centering: A Theory on Discourse Structure / 14.6：
Solving Coreferences / 14.7：
A Simplistic Method: Using Syntactic and Semantic Compatibility / 14.7.1：
Solving Coreferences with Shallow Grammatical Information / 14.7.2：
Salience in a Multimodal Context / 14.7.3：
Using a Machine-Learning Technique to Resolve Coreferences / 14.7.4：
More Complex Phenomena: Ellipses / 14.7.5：
Discourse and Rhetoric / 14.8：
Ancient Rhetoric: An Outline / 14.8.1：
Rhetorical Structure Theory / 14.8.2：
Types of Relations / 14.8.3：
Implementing Rhetorical Structure Theory / 14.8.4：
Events and Time / 14.9：
Events / 14.9.1：
Event Types / 14.9.2：
Temporal Representation of Events / 14.9.3：
Events and Tenses / 14.9.4：
Time ML, an Annotation Scheme for Time and Events / 14.10：
Dialogue / 14.11：
Why a Dialogue? / 15.1：
Simple Dialogue Systems / 15.3：
Dialogue Systems Based on Automata / 15.3.1：
Dialogue Modeling / 15.3.2：
Speech Acts: A Theory of Language Interaction / 15.4：
Speech Acts and Human-Machine Dialogue / 15.5：
Speech Acts as a Tagging Model / 15.5.1：
Speech Acts Tags Used in the SUNDIAL Project / 15.5.2：
Dialogue Parsing / 15.5.3：
Interpreting Speech Acts / 15.5.4：
EVAR: A Dialogue Application Using Speech Acts / 15.5.5：
Taking Beliefs and Intentions into Account / 15.6：
Representing Mental States / 15.6.1：
The STRIPS Planning Algorithm / 15.6.2：
Causality / 15.6.3：
An Introduction to Prolog / 15.7：
A Short Background / A.1：
Basic Features of Prolog / A.2：
Facts / A.2.1：
Terms / A.2.2：
Queries / A.2.3：
Logical Variables / A.2.4：
Shared Variables / A.2.5：
Data Types in Prolog / A.2.6：
Rules / A.2.7：
Running a Program / A.3：
Unification / A.4：
Substitution and Instances / A.4.1：
Terms and Unification / A.4.2：
The Herbrand Unification Algorithm / A.4.3：
Example / A.4.4：
The Occurs-Check / A.4.5：
Resolution / A.5：
Modus Ponens / A.5.1：
A Resolution Algorithm / A.5.2：
Derivation Trees and Backtracking / A.5.3：
Tracing and Debugging / A.6：
Cuts, Negation, and Related Predicates / A.7：
Cuts / A.7.1：
Negation / A.7.2：
The once/1 Predicate / A.7.3：
Lists / A.8：
Some List-Handling Predicates / A.9：
The member/2 Predicate / A.9.1：
The append/3 Predicate / A.9.2：
The delete/3 Predicate / A.9.3：
The intersection/3 Predicate / A.9.4：
The reverse/2 Predicate / A.9.5：
The Mode of an Argument / A.9.6：
Operators and Arithmetic / A.10：
Operators / A.10.1：
Arithmetic Operations / A.10.2：
Comparison Operators / A.10.3：
Lists and Arithmetic: The length/2 Predicate / A.10.4：
Lists and Comparison: The quicksort/2 Predicate / A.10.5：
Some Other Built-in Predicates / A.11：
Type Predicates / A.11.1：
Term Manipulation Predicates / A.11.2：
Handling Run-Time Errors and Exceptions / A.12：
Dynamically Accessing and Updatingthe Database / A.13：
Accessing a Clause: The clause/2 Predicate / A.13.1：
Dynamic and Static Predicates / A.13.2：
Adding a Clause: The asserta/1 and 1 assertz/Predicates / A.13.3：
Removing Clauses: The retract/1 and abolish/2 Predicates / A.13.4：
Handling Unknown Predicates / A.13.5：
All-Solutions Predicates / A.14：
Fundamental Search Algorithms / A.15：
Representing the Graph / A.15.1：
Depth-First Search / A.15.2：
Breadth-First Search / A.15.3：
A* Search / A.15.4：
Input/Output / A.16：
Reading and Writing Characters with Edinburgh Prolog / A.16.1：
Reading and Writing Terms with Edinburgh Prolog / A.16.2：
Opening and Closing Files with Edinburgh Prolog / A.16.3：
Reading and Writing Characters with Standard Prolog / A.16.4：
Reading and Writing Terms with Standard Prolog / A.16.5：
Opening and Closing Files with Standard Prolog / A.16.6：
Writing Loops / A.16.7：
Developing Prolog Programs / A.17：
Presentation Style / A.17.1：
Improving Programs / A.17.2：
Index
References
An Overview of Language Processing / 1：
Linguistics and Language Processing / 1.1：
Applications of Language Processing / 1.2：
24.

電子ブック
ＥＢ
24. An Introduction to Language Processing with Perl and Prolog
Pierre M. Nugues, A. Bundy, Jörg Siekmann
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2006
所蔵情報: loading…
目次情報: 続きを見る
An Overview of Language Processing / 1：
Linguistics and Language Processing / 1.1：
Applications of Language Processing / 1.2：
The Different Domains of Language Processing / 1.3：
Phonetics / 1.4：
Lexicon and Morphology / 1.5：
Syntax / 1.6：
Syntax as Defined by Noam Chomsky / 1.6.1：
Syntax as Relations and Dependencies / 1.6.2：
Semantics / 1.7：
Discourse and Dialogue / 1.8：
Why Speech and Language Processing Are Difficult / 1.9：
Ambiguity / 1.9.1：
Models and Their Implementation / 1.9.2：
An Example of Language Technology in Action: the Persona Project / 1.10：
Overview of Persona / 1.10.1：
The Persona's Modules / 1.10.2：
Further Reading / 1.11：
Corpus Processing Tools / 2：
Corpora / 2.1：
Types of Corpora / 2.1.1：
Corpora and Lexicon Building / 2.1.2：
Corpora as Knowledge Sources for the Linguist / 2.1.3：
Finite-State Automata / 2.2：
A Description / 2.2.1：
Mathematical Definition of Finite-State Automata / 2.2.2：
Finite-State Automata in Prolog / 2.2.3：
Deterministic and Nondeterministic Automata / 2.2.4：
Building a Deterministic Automata from a Nondeterministic One / 2.2.5：
Searching a String with a Finite-State Automaton / 2.2.6：
Operations on Finite-State Automata / 2.2.7：
Regular Expressions / 2.3：
Repetition Metacharacters / 2.3.1：
The Longest Match / 2.3.2：
Character Classes / 2.3.3：
Nonprintable Symbols or Positions / 2.3.4：
Union and Boolean Operators / 2.3.5：
Operator Combination and Precedence / 2.3.6：
Programming with Regular Expressions / 2.4：
Perl / 2.4.1：
Matching / 2.4.2：
Substitutions / 2.4.3：
Translating Characters / 2.4.4：
String Operators / 2.4.5：
Back References / 2.4.6：
Finding Concordances / 2.5：
Concordances in Prolog / 2.5.1：
Concordances in Perl / 2.5.2：
Approximate String Matching / 2.6：
Edit Operations / 2.6.1：
Minimum Edit Distance / 2.6.2：
Searching Edits in Prolog / 2.6.3：
Encoding, Entropy, and Annotation Schemes / 2.7：
Encoding Texts / 3.1：
Character Sets / 3.2：
Representing Characters / 3.2.1：
Unicode / 3.2.2：
The Unicode Encoding Schemes / 3.2.3：
Locales and Word Order / 3.3：
Presenting Time, Numerical Information, and Ordered Words / 3.3.1：
The Unicode Collation Algorithm / 3.3.2：
Markup Languages / 3.4：
A Brief Background / 3.4.1：
An Outline of XML / 3.4.2：
Writing a DTD / 3.4.3：
Writing an XML Document / 3.4.4：
Namespaces / 3.4.5：
Codes and Information Theory / 3.5：
Entropy / 3.5.1：
Huffman Encoding / 3.5.2：
Cross Entropy / 3.5.3：
Perplexity and Cross Perplexity / 3.5.4：
Entropy and Decision Trees / 3.6：
Decision Trees / 3.6.1：
Inducing Decision Trees Automatically / 3.6.2：
Counting Words / 3.7：
Counting Words and Word Sequences / 4.1：
Words and Tokens / 4.2：
What Is a Word? / 4.2.1：
Breaking a Text into Words: Tokenization / 4.2.2：
Tokenizing Texts / 4.3：
Tokenizing Texts in Prolog / 4.3.1：
Tokenizing Texts in Perl / 4.3.2：
N-grams / 4.4：
Some Definitions / 4.4.1：
Counting Unigrams in Prolog / 4.4.2：
Counting Unigrams with Perl / 4.4.3：
Counting Bigrams with Perl / 4.4.4：
Probabilistic Models of a Word Sequence / 4.5：
The Maximum Likelihood Estimation / 4.5.1：
Using ML Estimates with Nineteen Eighty-Four / 4.5.2：
Smoothing N-gram Probabilities / 4.6：
Sparse Data / 4.6.1：
Laplace's Rule / 4.6.2：
Good-Turing Estimation / 4.6.3：
Using N-grams of Variable Length / 4.7：
Linear Interpolation / 4.7.1：
Back-off / 4.7.2：
Quality of a Language Model / 4.8：
Intuitive Presentation / 4.8.1：
Entropy Rate / 4.8.2：
Perplexity / 4.8.3：
Collocations / 4.9：
Word Preference Measurements / 4.9.1：
Extracting Collocations with Perl / 4.9.2：
Application: Retrieval and Ranking of Documents on the Web / 4.10：
Words, Parts of Speech, and Morphology / 4.11：
Words / 5.1：
Parts of Speech / 5.1.1：
Features / 5.1.2：
Two Significant Parts of Speech: The Noun and the Verb / 5.1.3：
Lexicons / 5.2：
Encoding a Dictionary / 5.2.1：
Building a Trie in Prolog / 5.2.2：
Finding a Word in a Trie / 5.2.3：
Morphology / 5.3：
Morphemes / 5.3.1：
Morphs / 5.3.2：
Inflection and Derivation / 5.3.3：
Language Differences / 5.3.4：
Morphological Parsing / 5.4：
Two-Level Model of Morphology / 5.4.1：
Interpreting the Morphs / 5.4.2：
Finite-State Transducers / 5.4.3：
Conjugating a French Verb / 5.4.4：
Prolog Implementation / 5.4.5：
Operations on Finite-State Transducers / 5.4.6：
Morphological Rules / 5.5：
Two-Level Rules / 5.5.1：
Rules and Finite-State Transducers / 5.5.2：
Rule Composition: An Examplewith French Irregular Verbs / 5.5.3：
Application Examples / 5.6：
Part-of-Speech Tagging Using Rules / 5.7：
Resolving Part-of-Speech Ambiguity / 6.1：
A Manual Method / 6.1.1：
Which Method to Use to Automatically Assign Parts of Speech / 6.1.2：
Tagging with Rules / 6.2：
Brill's Tagger / 6.2.1：
Implementation in Prolog / 6.2.2：
Deriving Rules Automatically / 6.2.3：
Confusion Matrices / 6.2.4：
Unknown Words / 6.3：
Standardized Part-of-Speech Tagsets / 6.4：
Multilingual Part-of-Speech Tags / 6.4.1：
Parts of Speechfor English / 6.4.2：
An Annotation Schemefor Swedish / 6.4.3：
Part-of-Speech Tagging Using Stochastic Techniques / 6.5：
The Noisy Channel Model / 7.1：
Presentation / 7.1.1：
The N-gram Approximation / 7.1.2：
Tagging a Sentence / 7.1.3：
The Viterbi Algorithm: An Intuitive Presentation / 7.1.4：
Markov Models / 7.2：
Markov Chains / 7.2.1：
Hidden Markov Models / 7.2.2：
Three Fundamental Algorithms to Solve Problems with HMMs / 7.2.3：
The Forward Procedure / 7.2.4：
Viterbi Algorithm / 7.2.5：
The Backward Procedure / 7.2.6：
The Forward-Backward Algorithm / 7.2.7：
Tagging with Decision Trees / 7.3：
An Application of the Noisy Channel Model: Spell Checking / 7.4：
A Second Application: Language Models for Machine Translation / 7.6：
Parallel Corpora / 7.6.1：
Alignment / 7.6.2：
Translation / 7.6.3：
Phrase-Structure Grammars in Prolog / 7.7：
Using Prolog to Write Phrase-Structure Grammars / 8.1：
Representing Chomsky's Syntactic Formalism in Prolog / 8.2：
Constituents / 8.2.1：
Tree Structures / 8.2.2：
Phrase-Structure Rules / 8.2.3：
The Definite Clause Grammar (DCG) Notation / 8.2.4：
Parsing with DCGs / 8.3：
Translating DCGs into Prolog Clauses / 8.3.1：
Parsing and Generation / 8.3.2：
Left-Recursive Rules / 8.3.3：
Parsing Ambiguity / 8.4：
Using Variables / 8.5：
Gender and Number Agreement / 8.5.1：
Obtaining the Syntactic Structure / 8.5.2：
Application: Tokenizing Texts Using DCG Rules / 8.6：
Word Breaking / 8.6.1：
Recognition of Sentence Boundaries / 8.6.2：
Semantic Representation / 8.7：
A-Calculus / 8.7.1：
Embedding A-Expressions into DCG Rules / 8.7.2：
Semantic Composition of Verbs / 8.7.3：
An Application of Phrase-Structure Grammars and a Worked Example / 8.8：
Partial Parsing / 8.9：
Is Syntax Necessary? / 9.1：
Word Spotting and Template Matching / 9.2：
ELIZA / 9.2.1：
Word Spotting in Prolog / 9.2.2：
Multiword Detection / 9.3：
Multiwords / 9.3.1：
AStandard Multiword Annotation / 9.3.2：
Detecting Multiwords with Rules / 9.3.3：
Running the Program / 9.3.4：
Noun Groups and Verb Groups / 9.4：
Groups Versus Recursive Phrases / 9.4.1：
DCG Rules to Detect Noun Groups / 9.4.2：
DCG Rules to Detect Verb Groups / 9.4.3：
Running the Rules / 9.4.4：
Group Detection as a Tagging Problem / 9.5：
Tagging Gaps / 9.5.1：
Tagging Words / 9.5.2：
Using Symbolic Rules / 9.5.3：
Using Statistical Tagging / 9.5.4：
Cascading Partial Parsers / 9.6：
Elementary Analysis of Grammatical Functions / 9.7：
Main Functions / 9.7.1：
Extracting Other Groups / 9.7.2：
An Annotation Scheme for Groups in French / 9.8：
Application: The FASTUS System / 9.9：
The Message Understanding Conferences / 9.9.1：
The Syntactic Layers of the FASTUS System / 9.9.2：
Evaluationof Information Extraction Systems / 9.9.3：
Syntactic Formalisms / 9.10：
Introduction / 10.1：
Chomsky's Grammar in Syntactic Structures / 10.2：
Constituency: A Formal Definition / 10.2.1：
Transformations / 10.2.2：
Transformations and Movements / 10.2.3：
Gap Threading / 10.2.4：
Gap Threading to Parse Relative Clauses / 10.2.5：
Standardized Phrase Categories for English / 10.3：
Unification-Based Grammars / 10.4：
Representing Features in Prolog / 10.4.1：
A Formalism for Features and Rules / 10.4.3：
Features Organization / 10.4.4：
Features and Unification / 10.4.5：
A Unification Algorithm for Feature Structures / 10.4.6：
Dependency Grammars / 10.5：
Properties of a Dependency Graph / 10.5.1：
Valence / 10.5.3：
Dependencies and Functions / 10.5.4：
Parsing Techniques / 10.6：
Bottom-up Parsing / 11.1：
The Shift-Reduce Algorithm / 11.2.1：
Implementing Shift-Reduce Parsing in Prolog / 11.2.2：
Differences Between Bottom-up and Top-down Parsing / 11.2.3：
Chart Parsing / 11.3：
Backtracking and Efficiency / 11.3.1：
Structure of a Chart / 11.3.2：
The Active Chart / 11.3.3：
Modules of an Earley Parser / 11.3.4：
The Earley Algorithm in Prolog / 11.3.5：
The Earley Parser to Handle Left-Recursive Rules and Empty Symbols / 11.3.6：
Probabilistic Parsing of Context-Free Grammars / 11.4：
A Description of PCFGs / 11.5：
The Bottom-up Chart / 11.5.1：
The Cocke-Younger-Kasami Algorithm in Prolog / 11.5.2：
Adding Probabilities to the CYK Parser / 11.5.3：
Parser Evaluation / 11.6：
Constituency-Based Evaluation / 11.6.1：
Dependency-Based Evaluation / 11.6.2：
PerformanceofPCFG Parsing / 11.6.3：
Parsing Dependencies / 11.7：
Dependency Rules / 11.7.1：
Extending the Shift-Reduce Algorithm to Parse Dependencies / 11.7.2：
Nivre's Parser in Prolog / 11.7.3：
Finding Dependencies Using Constraints / 11.7.4：
Parsing Dependencies Using Statistical Techniques / 11.7.5：
Semantics and Predicate Logic / 11.8：
Language Meaning and Logic: An Illustrative Example / 12.1：
Formal Semantics / 12.3：
First-Order Predicate Calculus to Represent the State of Affairs / 12.4：
Variables and Constants / 12.4.1：
Predicates / 12.4.2：
Querying the Universe of Discourse / 12.5：
Mapping Phrases onto Logical Formulas / 12.6：
Representing Nouns and Adjectives / 12.6.1：
Representing Noun Groups / 12.6.2：
Representing Verbs and Prepositions / 12.6.3：
The Case of Determiners / 12.7：
Determiners and Logic Quantifiers / 12.7.1：
Translating Sentences Using Quantifiers / 12.7.2：
A General Representation of Sentences / 12.7.3：
Compositionality to Translate Phrases to Logical Forms / 12.8：
Translating the Noun Phrase / 12.8.1：
Translating the Verb Phrase / 12.8.2：
Augmenting the Database and Answering Questions / 12.9：
Declarations / 12.9.1：
Questions with Existential and Universal Quantifiers / 12.9.2：
Prolog and Unknown Predicates / 12.9.3：
Other Determiners and Questions / 12.9.4：
Application: The Spoken Language Translator / 12.10：
Translating Spoken Sentences / 12.10.1：
Compositional Semantics / 12.10.2：
Semantic Representation Transfer / 12.10.3：
Lexical Semantics / 12.11：
Beyond Formal Semantics / 13.1：
La langue etlaparole / 13.1.1：
Language and the Structure of the World / 13.1.2：
Lexical Structures / 13.2：
Some Basic Terms and Concepts / 13.2.1：
Ontological Organization / 13.2.2：
Lexical Classes and Relations / 13.2.3：
Semantic Networks / 13.2.4：
Building a Lexicon / 13.3：
The Lexicon and Word Senses / 13.3.1：
Verb Models / 13.3.2：
Definitions / 13.3.3：
An Example of Exhaustive Lexical Organization: Word Net / 13.4：
Nouns / 13.4.1：
Adjectives / 13.4.2：
Verbs / 13.4.3：
Automatic Word Sense Disambiguation / 13.5：
Senses as Tags / 13.5.1：
Associating a Word with a Context / 13.5.2：
Guessing the Topic / 13.5.3：
Naive Bayes / 13.5.4：
Using Constraints on Verbs / 13.5.5：
Using Dictionary Definitions / 13.5.6：
An Unsupervised Algorithm to Tag Senses / 13.5.7：
Senses and Languages / 13.5.8：
Case Grammars / 13.6：
Cases in Latin / 13.6.1：
Cases and Thematic Roles / 13.6.2：
Parsing with Cases / 13.6.3：
Semantic Grammars / 13.6.4：
Extending Case Grammars / 13.7：
Frame Net / 13.7.1：
A Statistical Method to Identify Semantic Roles / 13.7.2：
An Example of Case Grammar Application: EVAR / 13.8：
EVAR's Ontology and Syntactic Classes / 13.8.1：
Cases in EVAR / 13.8.2：
Discourse / 13.9：
Discourse: A Minimalist Definition / 14.1：
A Description of Discourse / 14.2.1：
Discourse Entities / 14.2.2：
References: An Application-Oriented View / 14.3：
References and Noun Phrases / 14.3.1：
Finding Names - Proper Nouns / 14.3.2：
Coreference / 14.4：
Anaphora / 14.4.1：
Solving Coreferences in an Example / 14.4.2：
A Standard Coreference Annotation / 14.4.3：
References: A More Formal View / 14.5：
Generating Discourse Entities: The Existential Quantifier / 14.5.1：
Retrieving Discourse Entities: Definite Descriptions / 14.5.2：
Generating Discourse Entities: The Universal Quantifier / 14.5.3：
Centering: A Theory on Discourse Structure / 14.6：
Solving Coreferences / 14.7：
A Simplistic Method: Using Syntactic and Semantic Compatibility / 14.7.1：
Solving Coreferences with Shallow Grammatical Information / 14.7.2：
Salience in a Multimodal Context / 14.7.3：
Using a Machine-Learning Technique to Resolve Coreferences / 14.7.4：
More Complex Phenomena: Ellipses / 14.7.5：
Discourse and Rhetoric / 14.8：
Ancient Rhetoric: An Outline / 14.8.1：
Rhetorical Structure Theory / 14.8.2：
Types of Relations / 14.8.3：
Implementing Rhetorical Structure Theory / 14.8.4：
Events and Time / 14.9：
Events / 14.9.1：
Event Types / 14.9.2：
Temporal Representation of Events / 14.9.3：
Events and Tenses / 14.9.4：
Time ML, an Annotation Scheme for Time and Events / 14.10：
Dialogue / 14.11：
Why a Dialogue? / 15.1：
Simple Dialogue Systems / 15.3：
Dialogue Systems Based on Automata / 15.3.1：
Dialogue Modeling / 15.3.2：
Speech Acts: A Theory of Language Interaction / 15.4：
Speech Acts and Human-Machine Dialogue / 15.5：
Speech Acts as a Tagging Model / 15.5.1：
Speech Acts Tags Used in the SUNDIAL Project / 15.5.2：
Dialogue Parsing / 15.5.3：
Interpreting Speech Acts / 15.5.4：
EVAR: A Dialogue Application Using Speech Acts / 15.5.5：
Taking Beliefs and Intentions into Account / 15.6：
Representing Mental States / 15.6.1：
The STRIPS Planning Algorithm / 15.6.2：
Causality / 15.6.3：
An Introduction to Prolog / 15.7：
A Short Background / A.1：
Basic Features of Prolog / A.2：
Facts / A.2.1：
Terms / A.2.2：
Queries / A.2.3：
Logical Variables / A.2.4：
Shared Variables / A.2.5：
Data Types in Prolog / A.2.6：
Rules / A.2.7：
Running a Program / A.3：
Unification / A.4：
Substitution and Instances / A.4.1：
Terms and Unification / A.4.2：
The Herbrand Unification Algorithm / A.4.3：
Example / A.4.4：
The Occurs-Check / A.4.5：
Resolution / A.5：
Modus Ponens / A.5.1：
A Resolution Algorithm / A.5.2：
Derivation Trees and Backtracking / A.5.3：
Tracing and Debugging / A.6：
Cuts, Negation, and Related Predicates / A.7：
Cuts / A.7.1：
Negation / A.7.2：
The once/1 Predicate / A.7.3：
Lists / A.8：
Some List-Handling Predicates / A.9：
The member/2 Predicate / A.9.1：
The append/3 Predicate / A.9.2：
The delete/3 Predicate / A.9.3：
The intersection/3 Predicate / A.9.4：
The reverse/2 Predicate / A.9.5：
The Mode of an Argument / A.9.6：
Operators and Arithmetic / A.10：
Operators / A.10.1：
Arithmetic Operations / A.10.2：
Comparison Operators / A.10.3：
Lists and Arithmetic: The length/2 Predicate / A.10.4：
Lists and Comparison: The quicksort/2 Predicate / A.10.5：
Some Other Built-in Predicates / A.11：
Type Predicates / A.11.1：
Term Manipulation Predicates / A.11.2：
Handling Run-Time Errors and Exceptions / A.12：
Dynamically Accessing and Updatingthe Database / A.13：
Accessing a Clause: The clause/2 Predicate / A.13.1：
Dynamic and Static Predicates / A.13.2：
Adding a Clause: The asserta/1 and 1 assertz/Predicates / A.13.3：
Removing Clauses: The retract/1 and abolish/2 Predicates / A.13.4：
Handling Unknown Predicates / A.13.5：
All-Solutions Predicates / A.14：
Fundamental Search Algorithms / A.15：
Representing the Graph / A.15.1：
Depth-First Search / A.15.2：
Breadth-First Search / A.15.3：
A* Search / A.15.4：
Input/Output / A.16：
Reading and Writing Characters with Edinburgh Prolog / A.16.1：
Reading and Writing Terms with Edinburgh Prolog / A.16.2：
Opening and Closing Files with Edinburgh Prolog / A.16.3：
Reading and Writing Characters with Standard Prolog / A.16.4：
Reading and Writing Terms with Standard Prolog / A.16.5：
Opening and Closing Files with Standard Prolog / A.16.6：
Writing Loops / A.16.7：
Developing Prolog Programs / A.17：
Presentation Style / A.17.1：
Improving Programs / A.17.2：
Index
References
An Overview of Language Processing / 1：
Linguistics and Language Processing / 1.1：
Applications of Language Processing / 1.2：
25.

電子ブック
ＥＢ
25. The Challenge of Anticipation
Giovanni Pezzulo, Martin V. Butz, Cristiano Castelfranchi, Rino Falcone, J?rg Siekmann
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2008
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目次情報: 続きを見る
Theory / Part I：
Introduction: Anticipation in Natural and Artificial Cognition / Giovanni Pezzulo ; Martin V. Butz ; Cristiano Castelfranchi ; Rino Falcone1：
Introduction / 1.1：
The Path to Anticipatory Cognitive Systems / 1.2：
Symbolic Behavior, Representation-Less Behavior, and Their Merge to Anticipatory Behavior / 1.2.1：
The Power of Anticipation: From Reactivity to Proactivity / 1.2.2：
The Anticipatory Approach to Cognitive Systems / 1.2.3：
The Unitary Nature of Anticipation / 1.2.4：
Anticipation in Living Organisms / 1.3：
Anticipatory Natural Cognition / 1.3.1：
Anticipatory Codes in the Brain / 1.3.2：
Simulative Theories of Cognition, and Their Unifying Nature / 1.3.3：
Conclusions / 1.4：
The Anticipatory Approach: Definitions and Taxonomies / 2：
Anticipatory Systems, Anticipation, and Anticipatory Behavior / 2.1：
Prediction vs. Anticipation / 2.2：
Predictive Capabilities / 2.2.1：
Anticipatory Capabilities / 2.2.2：
Anticipation and Goal-Oriented Behavior / 2.3：
The Anticipatory Structure of Goal-Oriented Behavior / 2.3.1：
Not All Anticipatory Behavior Is Goal-Oriented / 2.3.2：
Which Anticipations Permit Goal-Oriented Action? / 2.3.3：
The Hierarchical Organization of Anticipatory Goal-Oriented Action / 2.3.4：
Additional Elements of True Goal-Oriented Behavior / 2.3.5：
Anticipation and Learning / 2.4：
Learning to Predict / 2.4.1：
Bootstrapping Autonomous Cognitive Development: Surprise and Curiosity / 2.4.2：
From Willed to Automatic Control of Action and Vice Versa on the Basis of Surprise / 2.4.3：
Benefits of Anticipations in Cognitive Agents / 2.5：
Potentials for Anticipatory Systems / 3.1：
Potential Benefits of Anticipatory Mechanisms on Cognitive Functions / 3.2：
Effective, Context-Based Action Initiation / 3.2.1：
Faster and Smoother Behavior Execution / 3.2.2：
Improving Top-Down Attention / 3.2.3：
Improving Information Seeking / 3.2.4：
Improving Decision Making / 3.2.5：
Object Grounding, Categorization, and Ontologies / 3.2.6：
Social Abilities / 3.2.7：
Learning / 3.2.8：
Arising Challenges Due to Anticipations and Avoiding Them / 3.3：
Conclusion / 3.4：
Models, Architectures, and Applications / Part II：
Anticipation in Attention / Christian Balkenius ; Alexander Forster ; Birger Johansson ; Vin Thorsteinsdottir4：
Learning What to Look at / 4.1：
A Learning Saliency Map / 4.2.1：
Cue-Target Learning / 4.3：
Cueing by a Single Stimulus / 4.3.1：
Contextual Cueing / 4.3.2：
Fovea Based Solution / 4.3.3：
Attending to Moving Targets / 4.4：
Models of Smooth Pursuit / 4.4.1：
Engineering Approaches / 4.4.2：
The State Based Approach / 4.4.3：
The Prediction Approach / 4.4.4：
The Fovea Based Approach / 4.4.5：
Combining Bottom-Up and Top-Down Processes / 4.5：
Anticipatory, Goal-Directed Behavior / Oliver Herbort5：
A Brief History of Schemas / 5.1：
Schema Approaches / 5.2：
Symbolic Schemas for Policy Learning / 5.2.1：
Symbolic Schemas and Prediction for Selection / 5.2.2：
Neural-Based Planning / 5.2.3：
Neural Network-Based Dynamic Programming / 5.2.4：
Inverse Model Approaches / 5.3：
Inverse Models in Motor Learning and Control / 5.3.1：
Inverse Models and Schema Approaches / 5.3.2：
Advanced Structures / 5.4：
Prediction and Action / 5.4.1：
Coupled Forward-Inverse Models / 5.4.2：
Hierarchical Anticipatory Systems / 5.4.3：
Evaluation of Predictive and Anticipatory Capabilities / 5.5：
Schema-Based Systems / 5.5.1：
Discussion / 5.5.2：
Contrasting Predictive System Capabilities / 5.6.1：
Contrasting Anticipatory System Capabilities / 5.6.2：
Integration / 5.6.3：
Anticipation and Believability / Carlos Martinho ; Ana Paiva5.7：
Animation and Believability / 6.1：
Emotion and Exaggeration / 6.1.2：
Anticipation / 6.1.3：
Anticipation, Emotion, and Believability / 6.1.4：
Related Work / 6.2：
Oz Project / 6.2.1：
EMA / 6.2.2：
Duncan the Highland Terrier / 6.2.3：
Emotivector / 6.3：
Architecture / 6.3.1：
Anticipation Model / 6.3.2：
Salience Model / 6.3.3：
Sensation Model / 6.3.4：
Selection Model / 6.3.5：
Uncertainty / 6.3.6：
Aini, the Synthetic Flower / 6.4：
Emotivectors in Action / 6.4.1：
Evaluation / 6.4.2：
iCat, the Affective Game Buddy / 6.5：
Emotivector Integration in Agent Architectures / 6.5.1：
Anticipation and Emotions for Goal Directed Agents / Emiliano Lorini ; Michele Piunti ; Maria Miceli6.7：
Related Works in Affective Computing / 7.1：
Expectations and Surprise / 7.3：
A Typology of Expectations and Predictions / 7.3.1：
From the Typology of Expectations to the Typology of Surprise / 7.3.2：
Roles of Surprise in Cognitive Processing / 7.3.3：
Expectations and Emotions for Goal-Directed Agents / 7.4：
Expectations and Decision Making / 7.4.1：
Situated Agents and Affective States / 7.4.2：
Confidence of Predictions and Modulation of the Probability Function / 7.4.3：
A Reinforcement-Learning Model of Top-Down Attention Based on a Potential-Action Map / Dimitri Ognibene ; Gianluca Baldassarre7.4.4：
Methods / 8.1：
RGB Camera Input / 8.2.1：
Saliency Map and Action Selection / 8.2.2：
Fovea / 8.2.3：
Periphery Map / 8.2.4：
Inhibition-of-Return Map / 8.2.5：
Potential Action Map / 8.2.6：
Actor-Critic Model / 8.2.7：
Parameter Settings / 8.2.8：
The Tasks / 8.2.9：
Results / 8.3：
Learning and Performance of the Models / 8.3.1：
Bottom-Up Attention: Periphery Map and Inhibition-of-Return Map / 8.3.2：
Analysis of the Vote Maps / 8.3.3：
Capability of Learning to Stay, and of Staying, on the Target / 8.3.4：
Potential Action Map: An Action-Oriented Memory of Cue Information / 8.3.5：
Potential Action Map: Capacity to Integrate Multiple Sources of Information / 8.3.6：
Anticipation by Analogy / Boicho Kokinov ; Maurice Grinberg ; Georgi Petkov ; Kiril Kiryazov8.4：
The Anticipation by Analogy Scenario / 9.1：
Models of Analogy-Making / 9.3：
AMBR Model of Analogy-Making / 9.4：
Integrating Visual Perception and Motor Control in AMBR / 9.5：
Top-Down Perception / 9.5.1：
Attention / 9.5.2：
Transfer of the Solution / 9.5.3：
Action Execution / 9.5.4：
Running the Simulated Model and Comparing It with Human Data / 9.6：
Comparing with Human Data / 9.6.1：
Running the Real Robot Model in the Real World / 9.7：
Ikaros / 9.7.1：
AMBR2Robot / 9.7.2：
Tests / 9.7.3：
Mechanisms for Active Vision / 9.8：
Discussion and Conclusion / 9.9：
Anticipation in Coordination / Emilian Lalev10：
The Prisoner's Dilemma Game / 10.1：
Related Research / 10.2：
Fictitious Play / 10.2.1：
Strategic Teaching and Reputation Formation / 10.2.2：
Social Order and Coordination / 10.2.3：
Anticipation and Information Processing in Societies / 10.2.4：
Agent Architecture and Decision Making Model / 10.3：
The Model / 10.3.1：
Judgment and Decision Making / 10.3.2：
Game Simulations with Individual Agents: Comparison with Experimental Results / 10.4：
Comparison of the Model with Experimental Results / 10.4.1：
Multi-Agent Simulations / 10.5：
Agent Societies / 10.5.1：
Simulation Results and Discussions / 10.5.2：
Endowing Artificial Systems with Anticipatory Capabilities: Success Cases / 10.6：
Flexible Goal-Directed Arm Control: The SURE_REACH Architecture / 11.1：
Learning Cognitive Maps for Anticipatory Control: Time Growing Neural Gas / 11.3：
Learning Effective Directional Arm Control: The Evolutionary System XCSF / 11.4：
Anticipatory Target Motion Prediction / 11.5：
Anticipatory Spatial Attention with Saliency Maps / 11.6：
Behavior Prediction in a Group of Robots / 11.7：
Enhanced Adaptivity in a Predator-Prey Scenario / 11.8：
Adaptive Navigation and Control with Anticipation / 11.9：
Mental Experiments for Selecting Actions / 11.10：
Anticipations for Believable Behavior / 11.11：
Anticipatory Behavior in a Searching-for-an-Object Task / 11.12：
The Role of Anticipation in Cooperation and Coordination / 11.13：
Anticipatory Effects of Expectations and Emotions / 11.14：
On-Line and Off-Line Anticipation for Action Control / 11.15：
References / 11.16：
Theory / Part I：
Introduction: Anticipation in Natural and Artificial Cognition / Giovanni Pezzulo ; Martin V. Butz ; Cristiano Castelfranchi ; Rino Falcone1：
Introduction / 1.1：
26.

電子ブック
ＥＢ
26. The Challenge of Anticipation
Giovanni Pezzulo, Martin V. Butz, Cristiano Castelfranchi, Rino Falcone, Jörg Siekmann
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2008
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目次情報: 続きを見る
Theory / Part I：
Introduction: Anticipation in Natural and Artificial Cognition / Giovanni Pezzulo ; Martin V. Butz ; Cristiano Castelfranchi ; Rino Falcone1：
Introduction / 1.1：
The Path to Anticipatory Cognitive Systems / 1.2：
Symbolic Behavior, Representation-Less Behavior, and Their Merge to Anticipatory Behavior / 1.2.1：
The Power of Anticipation: From Reactivity to Proactivity / 1.2.2：
The Anticipatory Approach to Cognitive Systems / 1.2.3：
The Unitary Nature of Anticipation / 1.2.4：
Anticipation in Living Organisms / 1.3：
Anticipatory Natural Cognition / 1.3.1：
Anticipatory Codes in the Brain / 1.3.2：
Simulative Theories of Cognition, and Their Unifying Nature / 1.3.3：
Conclusions / 1.4：
The Anticipatory Approach: Definitions and Taxonomies / 2：
Anticipatory Systems, Anticipation, and Anticipatory Behavior / 2.1：
Prediction vs. Anticipation / 2.2：
Predictive Capabilities / 2.2.1：
Anticipatory Capabilities / 2.2.2：
Anticipation and Goal-Oriented Behavior / 2.3：
The Anticipatory Structure of Goal-Oriented Behavior / 2.3.1：
Not All Anticipatory Behavior Is Goal-Oriented / 2.3.2：
Which Anticipations Permit Goal-Oriented Action? / 2.3.3：
The Hierarchical Organization of Anticipatory Goal-Oriented Action / 2.3.4：
Additional Elements of True Goal-Oriented Behavior / 2.3.5：
Anticipation and Learning / 2.4：
Learning to Predict / 2.4.1：
Bootstrapping Autonomous Cognitive Development: Surprise and Curiosity / 2.4.2：
From Willed to Automatic Control of Action and Vice Versa on the Basis of Surprise / 2.4.3：
Benefits of Anticipations in Cognitive Agents / 2.5：
Potentials for Anticipatory Systems / 3.1：
Potential Benefits of Anticipatory Mechanisms on Cognitive Functions / 3.2：
Effective, Context-Based Action Initiation / 3.2.1：
Faster and Smoother Behavior Execution / 3.2.2：
Improving Top-Down Attention / 3.2.3：
Improving Information Seeking / 3.2.4：
Improving Decision Making / 3.2.5：
Object Grounding, Categorization, and Ontologies / 3.2.6：
Social Abilities / 3.2.7：
Learning / 3.2.8：
Arising Challenges Due to Anticipations and Avoiding Them / 3.3：
Conclusion / 3.4：
Models, Architectures, and Applications / Part II：
Anticipation in Attention / Christian Balkenius ; Alexander Forster ; Birger Johansson ; Vin Thorsteinsdottir4：
Learning What to Look at / 4.1：
A Learning Saliency Map / 4.2.1：
Cue-Target Learning / 4.3：
Cueing by a Single Stimulus / 4.3.1：
Contextual Cueing / 4.3.2：
Fovea Based Solution / 4.3.3：
Attending to Moving Targets / 4.4：
Models of Smooth Pursuit / 4.4.1：
Engineering Approaches / 4.4.2：
The State Based Approach / 4.4.3：
The Prediction Approach / 4.4.4：
The Fovea Based Approach / 4.4.5：
Combining Bottom-Up and Top-Down Processes / 4.5：
Anticipatory, Goal-Directed Behavior / Oliver Herbort5：
A Brief History of Schemas / 5.1：
Schema Approaches / 5.2：
Symbolic Schemas for Policy Learning / 5.2.1：
Symbolic Schemas and Prediction for Selection / 5.2.2：
Neural-Based Planning / 5.2.3：
Neural Network-Based Dynamic Programming / 5.2.4：
Inverse Model Approaches / 5.3：
Inverse Models in Motor Learning and Control / 5.3.1：
Inverse Models and Schema Approaches / 5.3.2：
Advanced Structures / 5.4：
Prediction and Action / 5.4.1：
Coupled Forward-Inverse Models / 5.4.2：
Hierarchical Anticipatory Systems / 5.4.3：
Evaluation of Predictive and Anticipatory Capabilities / 5.5：
Schema-Based Systems / 5.5.1：
Discussion / 5.5.2：
Contrasting Predictive System Capabilities / 5.6.1：
Contrasting Anticipatory System Capabilities / 5.6.2：
Integration / 5.6.3：
Anticipation and Believability / Carlos Martinho ; Ana Paiva5.7：
Animation and Believability / 6.1：
Emotion and Exaggeration / 6.1.2：
Anticipation / 6.1.3：
Anticipation, Emotion, and Believability / 6.1.4：
Related Work / 6.2：
Oz Project / 6.2.1：
EMA / 6.2.2：
Duncan the Highland Terrier / 6.2.3：
Emotivector / 6.3：
Architecture / 6.3.1：
Anticipation Model / 6.3.2：
Salience Model / 6.3.3：
Sensation Model / 6.3.4：
Selection Model / 6.3.5：
Uncertainty / 6.3.6：
Aini, the Synthetic Flower / 6.4：
Emotivectors in Action / 6.4.1：
Evaluation / 6.4.2：
iCat, the Affective Game Buddy / 6.5：
Emotivector Integration in Agent Architectures / 6.5.1：
Anticipation and Emotions for Goal Directed Agents / Emiliano Lorini ; Michele Piunti ; Maria Miceli6.7：
Related Works in Affective Computing / 7.1：
Expectations and Surprise / 7.3：
A Typology of Expectations and Predictions / 7.3.1：
From the Typology of Expectations to the Typology of Surprise / 7.3.2：
Roles of Surprise in Cognitive Processing / 7.3.3：
Expectations and Emotions for Goal-Directed Agents / 7.4：
Expectations and Decision Making / 7.4.1：
Situated Agents and Affective States / 7.4.2：
Confidence of Predictions and Modulation of the Probability Function / 7.4.3：
A Reinforcement-Learning Model of Top-Down Attention Based on a Potential-Action Map / Dimitri Ognibene ; Gianluca Baldassarre7.4.4：
Methods / 8.1：
RGB Camera Input / 8.2.1：
Saliency Map and Action Selection / 8.2.2：
Fovea / 8.2.3：
Periphery Map / 8.2.4：
Inhibition-of-Return Map / 8.2.5：
Potential Action Map / 8.2.6：
Actor-Critic Model / 8.2.7：
Parameter Settings / 8.2.8：
The Tasks / 8.2.9：
Results / 8.3：
Learning and Performance of the Models / 8.3.1：
Bottom-Up Attention: Periphery Map and Inhibition-of-Return Map / 8.3.2：
Analysis of the Vote Maps / 8.3.3：
Capability of Learning to Stay, and of Staying, on the Target / 8.3.4：
Potential Action Map: An Action-Oriented Memory of Cue Information / 8.3.5：
Potential Action Map: Capacity to Integrate Multiple Sources of Information / 8.3.6：
Anticipation by Analogy / Boicho Kokinov ; Maurice Grinberg ; Georgi Petkov ; Kiril Kiryazov8.4：
The Anticipation by Analogy Scenario / 9.1：
Models of Analogy-Making / 9.3：
AMBR Model of Analogy-Making / 9.4：
Integrating Visual Perception and Motor Control in AMBR / 9.5：
Top-Down Perception / 9.5.1：
Attention / 9.5.2：
Transfer of the Solution / 9.5.3：
Action Execution / 9.5.4：
Running the Simulated Model and Comparing It with Human Data / 9.6：
Comparing with Human Data / 9.6.1：
Running the Real Robot Model in the Real World / 9.7：
Ikaros / 9.7.1：
AMBR2Robot / 9.7.2：
Tests / 9.7.3：
Mechanisms for Active Vision / 9.8：
Discussion and Conclusion / 9.9：
Anticipation in Coordination / Emilian Lalev10：
The Prisoner's Dilemma Game / 10.1：
Related Research / 10.2：
Fictitious Play / 10.2.1：
Strategic Teaching and Reputation Formation / 10.2.2：
Social Order and Coordination / 10.2.3：
Anticipation and Information Processing in Societies / 10.2.4：
Agent Architecture and Decision Making Model / 10.3：
The Model / 10.3.1：
Judgment and Decision Making / 10.3.2：
Game Simulations with Individual Agents: Comparison with Experimental Results / 10.4：
Comparison of the Model with Experimental Results / 10.4.1：
Multi-Agent Simulations / 10.5：
Agent Societies / 10.5.1：
Simulation Results and Discussions / 10.5.2：
Endowing Artificial Systems with Anticipatory Capabilities: Success Cases / 10.6：
Flexible Goal-Directed Arm Control: The SURE_REACH Architecture / 11.1：
Learning Cognitive Maps for Anticipatory Control: Time Growing Neural Gas / 11.3：
Learning Effective Directional Arm Control: The Evolutionary System XCSF / 11.4：
Anticipatory Target Motion Prediction / 11.5：
Anticipatory Spatial Attention with Saliency Maps / 11.6：
Behavior Prediction in a Group of Robots / 11.7：
Enhanced Adaptivity in a Predator-Prey Scenario / 11.8：
Adaptive Navigation and Control with Anticipation / 11.9：
Mental Experiments for Selecting Actions / 11.10：
Anticipations for Believable Behavior / 11.11：
Anticipatory Behavior in a Searching-for-an-Object Task / 11.12：
The Role of Anticipation in Cooperation and Coordination / 11.13：
Anticipatory Effects of Expectations and Emotions / 11.14：
On-Line and Off-Line Anticipation for Action Control / 11.15：
References / 11.16：
Theory / Part I：
Introduction: Anticipation in Natural and Artificial Cognition / Giovanni Pezzulo ; Martin V. Butz ; Cristiano Castelfranchi ; Rino Falcone1：
Introduction / 1.1：
27.

電子ブック
ＥＢ
27. The Path to Autonomous Robots
Gaurav Sukhatme
出版情報: Springer eBooks Computer Science , Springer US, 2009
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Recent Research in Autonomous Robots / Part I：
Mobile Robots for Polar Remote Sensing / Christopher M. Gifford ; Eric L. Akers ; Richard S. Stansbury ; Arvin Agah1：
Introduction / 1.1：
Polar Mobile Robots / 1.2：
Challenges and Survivability Issues for Polar Robotics / 1.2.1：
MARVIN I / 1.2.2：
MARVIN II / 1.2.3：
Software Architecture / 1.2.4：
North Greenland Ice Core Project (GRIP) Camp Operations / 1.2.5：
Summit Camp Operations / 1.2.6：
West Antarctic Ice Sheet (WAIS) Divide Camp Operations / 1.2.7：
Robotics-Based Approaches to Seismic Surveying / 1.3：
Related Work / 1.3.1：
Robotics-Based Approaches / 1.3.2：
Conclusion / 1.4：
Guidance and Control of Formation Flying Spacecraft / F. Y. Hadaegh ; G. Singh ; B. Acikmese ; D. P. Scharf ; M. Mandic2：
Modeling and Simulation / 2.1：
Guidance and Control Architectures / 2.3：
Formation State Estimation / 2.4：
Guidance and Control / 2.5：
Formulation of Optimal Path Planning Problem / 2.5.1：
Conclusions / 2.6：
Acknowledgement / 2.7：
Adaptive Sampling for Field Reconstruction With Multiple Mobile Robots / Bin Zhang ; Gaurav S. Sukhatme3：
Adaptive Sampling / 3.1：
Divide and Conquer / 3.4：
Discretization / 3.4.1：
Graph Partition / 3.4.2：
Path Planning for a Single Robot / 3.4.3：
Simulations / 3.5：
Conclusion and Future Work / 3.6：
Grasping Affordances: Learning to Connect Vision to Hand Action / Charles de Granville ; Di Wang ; Joshua Southerland ; Robert Platt, Jr. ; Andrew H. Fagg4：
Learning Models of 3D Object Appearance / 4.1：
Edgel Constellations for Describing 2D Object Appearance / 4.2.1：
Capturing Object Appearance in 3D / 4.2.2：
Learning Complete 3D Appearance Models / 4.2.3：
Data Collection and Preprocessing / 4.2.4：
Experimental Results / 4.2.5：
Learning Canonical Grasps for Objects / 4.3：
Modeling Hand Orientation / 4.3.1：
Modeling Hand Position / 4.3.2：
Modeling Finger Posture / 4.3.3：
Modeling Mixtures of Hand Postures / 4.3.4：
Data Collection / 4.3.5：
Discussion / 4.3.6：
Intelligent Robotics for Assistive Healtheare and Therapy / Ayanna M. Howard ; Sekou Remy ; Chung Hyuk Park ; Hae Won Park ; Douglas Brooks5：
Activities of Daily Living: Robot Learning from Human Teleoperation / 5.1：
Divided Force Guidance for Haptic Feedback / 5.2.1：
Learning through Haptically Guided Manipulation / 5.2.2：
Experiments / 5.2.3：
Child Therapy and Education: Robots in Interactive Play Scenarios / 5.3：
Defining Play Primitives / 5.3.1：
Physical Therapy: Robot Assistance via Patient Observation / 5.3.2：
Learning of Exercise Primitives / 5.4.1：
Learning of Exercise Behaviors / 5.4.2：
A New Direction in Human-Robot Interaction: A Lesson from Star Wars? / Gerard Jounghyun Kim5.4.3：
Indirect Human-Robot Interaction / 6.1：
Robot location/pose tracking / 6.2.1：
User/environment sensing / 6.2.2：
Flexible projection / 6.2.3：
Large display surface centered interaction design / 6.2.4：
Summary and Postscript / 6.3：
Neurorobotics Primer / M. Anthony Lewis ; Theresa J. Klein7：
Neurorobots and the Scientific Method / 7.1：
21st Century Robotics: Productizing Mythology / 7.1.2：
Computational Substrate / 7.1.3：
Neuromorphic Chips / 7.1.4：
Graphics Processing Units / 7.1.5：
Purpose of this Chapter / 7.1.6：
Classical Robotics / 7.2：
Configuration Space / 7.2.1：
Kinematics / 7.2.2：
Differential Motion / 7.2.3：
Statics / 7.2.4：
Redundancy / 7.2.5：
Dynamics / 7.2.6：
Trajectory Generation / 7.2.7：
A Pause to Reflect / 7.2.8：
Basic Neurocomputation / 7.3：
Information Flows into Dendrites and Out of Axons / 7.3.1：
The Neuron Cell is a Capacitor with a Decision Making Capability / 7.3.2：
Neural Models Capture the Basic Dynamics of the Cell Body and Throw Away Some Details / 7.3.3：
Numerical Integration / 7.3.4：
Reflexes and High Level Control / 7.3.5 Building Neural Oscillators: Nature's Coordination and Trajectory Generation Mechanism：
Notable Systems / 7.4：
GPUs / 7.5：
Learning Inverse Dynamics by Gaussian Process Regression under the Multi-Task Learning Framework / Dit-Yan Yeung ; Yu Zhang7.6：
Appreciation and Dedication / 8.1：
Kinematics and Dynamics / 8.2 Robotic Control：
Reasons Against Analytic Solutions / 8.2.2：
Insights from Human Arm Control / 8.2.3：
Learning and Control / 8.2.4：
Learning Inverse Dynamics / 8.3：
Recent Work / 8.3.1：
Learning Inverse Dynamics as a Regression Problem / 8.3.2：
Gaussian Process Regression / 8.4：
Brief Review / 8.4.1：
Gaussian Process Regression for Learning Inverse Dynamics / 8.4.2：
Multi-Task Gaussian Process Regression / 8.5：
Brief Review of Bonilla et al.'s Method (33) / 8.5.1：
Multi-Task Gaussian Process Regression for Learning Inverse Dynamics / 8.5.2：
Tributes and Recollections from Former Students / 8.6：
Professor George Albert Bekey / 9：
Personal Life / 9.1：
Research / 9.2：
Teaching and Students / 9.3：
Service to the University and the Profession / 9.4：
Recognition, Honors, and Awards / 9.5：
A Personal Tribute / 9.6：
Current History of the Bekey Tribe / H. Pete Schmid ; Monte Ung10：
Recollections and Tributes / Dan Antonelli ; Arun Bhadoria ; Willis G. Downing, Jr. ; Huan Liu ; Michael Merritt ; L. Warren Morrison11：
From Aerospace Engineering to Biomedical Engineering / 11.1：
The Final Oral Examination / 11.2：
Recent Work on Preventing Fractures caused by a Fall / 11.3：
Teacher, Mentor, and Friend / 11.4：
A Testimonial / 11.5：
Making it Look Easy / 11.6：
Solving Complex Problems Efficiently / 11.7：
References
Index
Recent Research in Autonomous Robots / Part I：
Mobile Robots for Polar Remote Sensing / Christopher M. Gifford ; Eric L. Akers ; Richard S. Stansbury ; Arvin Agah1：
Introduction / 1.1：
28.

電子ブック
ＥＢ
28. The Path to Autonomous Robots
Gaurav Sukhatme, Gaurav S. Sukhatme
出版情報: SpringerLink Books - AutoHoldings , Springer US, 2009
所蔵情報: loading…
目次情報: 続きを見る
Recent Research in Autonomous Robots / Part I：
Mobile Robots for Polar Remote Sensing / Christopher M. Gifford ; Eric L. Akers ; Richard S. Stansbury ; Arvin Agah1：
Introduction / 1.1：
Polar Mobile Robots / 1.2：
Challenges and Survivability Issues for Polar Robotics / 1.2.1：
MARVIN I / 1.2.2：
MARVIN II / 1.2.3：
Software Architecture / 1.2.4：
North Greenland Ice Core Project (GRIP) Camp Operations / 1.2.5：
Summit Camp Operations / 1.2.6：
West Antarctic Ice Sheet (WAIS) Divide Camp Operations / 1.2.7：
Robotics-Based Approaches to Seismic Surveying / 1.3：
Related Work / 1.3.1：
Robotics-Based Approaches / 1.3.2：
Conclusion / 1.4：
Guidance and Control of Formation Flying Spacecraft / F. Y. Hadaegh ; G. Singh ; B. Acikmese ; D. P. Scharf ; M. Mandic2：
Modeling and Simulation / 2.1：
Guidance and Control Architectures / 2.3：
Formation State Estimation / 2.4：
Guidance and Control / 2.5：
Formulation of Optimal Path Planning Problem / 2.5.1：
Conclusions / 2.6：
Acknowledgement / 2.7：
Adaptive Sampling for Field Reconstruction With Multiple Mobile Robots / Bin Zhang ; Gaurav S. Sukhatme3：
Adaptive Sampling / 3.1：
Divide and Conquer / 3.4：
Discretization / 3.4.1：
Graph Partition / 3.4.2：
Path Planning for a Single Robot / 3.4.3：
Simulations / 3.5：
Conclusion and Future Work / 3.6：
Grasping Affordances: Learning to Connect Vision to Hand Action / Charles de Granville ; Di Wang ; Joshua Southerland ; Robert Platt, Jr. ; Andrew H. Fagg4：
Learning Models of 3D Object Appearance / 4.1：
Edgel Constellations for Describing 2D Object Appearance / 4.2.1：
Capturing Object Appearance in 3D / 4.2.2：
Learning Complete 3D Appearance Models / 4.2.3：
Data Collection and Preprocessing / 4.2.4：
Experimental Results / 4.2.5：
Learning Canonical Grasps for Objects / 4.3：
Modeling Hand Orientation / 4.3.1：
Modeling Hand Position / 4.3.2：
Modeling Finger Posture / 4.3.3：
Modeling Mixtures of Hand Postures / 4.3.4：
Data Collection / 4.3.5：
Discussion / 4.3.6：
Intelligent Robotics for Assistive Healtheare and Therapy / Ayanna M. Howard ; Sekou Remy ; Chung Hyuk Park ; Hae Won Park ; Douglas Brooks5：
Activities of Daily Living: Robot Learning from Human Teleoperation / 5.1：
Divided Force Guidance for Haptic Feedback / 5.2.1：
Learning through Haptically Guided Manipulation / 5.2.2：
Experiments / 5.2.3：
Child Therapy and Education: Robots in Interactive Play Scenarios / 5.3：
Defining Play Primitives / 5.3.1：
Physical Therapy: Robot Assistance via Patient Observation / 5.3.2：
Learning of Exercise Primitives / 5.4.1：
Learning of Exercise Behaviors / 5.4.2：
A New Direction in Human-Robot Interaction: A Lesson from Star Wars? / Gerard Jounghyun Kim5.4.3：
Indirect Human-Robot Interaction / 6.1：
Robot location/pose tracking / 6.2.1：
User/environment sensing / 6.2.2：
Flexible projection / 6.2.3：
Large display surface centered interaction design / 6.2.4：
Summary and Postscript / 6.3：
Neurorobotics Primer / M. Anthony Lewis ; Theresa J. Klein7：
Neurorobots and the Scientific Method / 7.1：
21st Century Robotics: Productizing Mythology / 7.1.2：
Computational Substrate / 7.1.3：
Neuromorphic Chips / 7.1.4：
Graphics Processing Units / 7.1.5：
Purpose of this Chapter / 7.1.6：
Classical Robotics / 7.2：
Configuration Space / 7.2.1：
Kinematics / 7.2.2：
Differential Motion / 7.2.3：
Statics / 7.2.4：
Redundancy / 7.2.5：
Dynamics / 7.2.6：
Trajectory Generation / 7.2.7：
A Pause to Reflect / 7.2.8：
Basic Neurocomputation / 7.3：
Information Flows into Dendrites and Out of Axons / 7.3.1：
The Neuron Cell is a Capacitor with a Decision Making Capability / 7.3.2：
Neural Models Capture the Basic Dynamics of the Cell Body and Throw Away Some Details / 7.3.3：
Numerical Integration / 7.3.4：
Reflexes and High Level Control / 7.3.5 Building Neural Oscillators: Nature's Coordination and Trajectory Generation Mechanism：
Notable Systems / 7.4：
GPUs / 7.5：
Learning Inverse Dynamics by Gaussian Process Regression under the Multi-Task Learning Framework / Dit-Yan Yeung ; Yu Zhang7.6：
Appreciation and Dedication / 8.1：
Kinematics and Dynamics / 8.2 Robotic Control：
Reasons Against Analytic Solutions / 8.2.2：
Insights from Human Arm Control / 8.2.3：
Learning and Control / 8.2.4：
Learning Inverse Dynamics / 8.3：
Recent Work / 8.3.1：
Learning Inverse Dynamics as a Regression Problem / 8.3.2：
Gaussian Process Regression / 8.4：
Brief Review / 8.4.1：
Gaussian Process Regression for Learning Inverse Dynamics / 8.4.2：
Multi-Task Gaussian Process Regression / 8.5：
Brief Review of Bonilla et al.'s Method (33) / 8.5.1：
Multi-Task Gaussian Process Regression for Learning Inverse Dynamics / 8.5.2：
Tributes and Recollections from Former Students / 8.6：
Professor George Albert Bekey / 9：
Personal Life / 9.1：
Research / 9.2：
Teaching and Students / 9.3：
Service to the University and the Profession / 9.4：
Recognition, Honors, and Awards / 9.5：
A Personal Tribute / 9.6：
Current History of the Bekey Tribe / H. Pete Schmid ; Monte Ung10：
Recollections and Tributes / Dan Antonelli ; Arun Bhadoria ; Willis G. Downing, Jr. ; Huan Liu ; Michael Merritt ; L. Warren Morrison11：
From Aerospace Engineering to Biomedical Engineering / 11.1：
The Final Oral Examination / 11.2：
Recent Work on Preventing Fractures caused by a Fall / 11.3：
Teacher, Mentor, and Friend / 11.4：
A Testimonial / 11.5：
Making it Look Easy / 11.6：
Solving Complex Problems Efficiently / 11.7：
References
Index
Recent Research in Autonomous Robots / Part I：
Mobile Robots for Polar Remote Sensing / Christopher M. Gifford ; Eric L. Akers ; Richard S. Stansbury ; Arvin Agah1：
Introduction / 1.1：
29.

電子ブック
ＥＢ
29. Bee-Inspired Protocol Engineering
Muddassar Farooq, Th B?ck, A. E. Eiben, G. Rozenberg
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2009
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Introduction / 1：
Motivation of the Work / 1.1：
Problem Statement / 1.2：
Hypotheses / 1.2.1：
An Engineering Approach to Nature-Inspired Routing Protocols / 1.3：
The Scientific Contributions of the Work / 1.4：
A Simple, Disributed, Decentralized Multi-Agent System / 1.4.1：
A Comprehensive Routing System / 1.4.2：
An Empirical Comprehensive Performance Evaluation Framework / 1.4.3：
A Scalability Framework for (Nature-Inspired) Agent-Based Routing Protocols / 1.4.4：
Protocol Engineering of Nature-Inspired Routing Protocols / 1.4.5：
A Nature-Inspired Linux Router / 1.4.6：
The Protocol Validation Framework / 1.4.7：
The Formal Framework for Nature-Inspired Protocols / 1.4.8：
A Simple, Efficient, and Scalable Nature-Inspired Security Framework / 1.4.9：
Emerging Mobile and Wireless Sensors Ad Hoc Networks / 1.4.10：
Organization of the Book / 1.5：
A Comprehensive Survey of Nature-Inspired Routing Protocols / 2：
Organization of the Chapter / 2.1：
Network Routing Algorithms / 2.2：
Features Landscape of a Modern Routing Algorithm / 2.2.1：
Taxonomy of Routing Algorithms / 2.2.2：
Ant Colony Optimization (ACO) Routing Algorithms for Fixed Networks / 2.3：
Important Elements of ACO in Routing / 2.3.1：
Ant-Based Control (ABC) for Circuit-Switched Networks / 2.3.2：
Ant-Based Control (ABC) for Packet-Switched Networks / 2.3.3：
AntNet / 2.3.4：
Ant Colony Routing (ACR) and AntNet+SELA QoS-Aware Routing / 2.3.5：
A Brief History of Research in AntNet / 2.3.6：
Evolutionary Routing Algorithms for Fixed Networks / 2.4：
Important Elements of EA in Routing / 2.4.1：
GARA / 2.4.2：
ASGA and SynthECA / 2.4.3：
DGA / 2.4.4：
Related Work on Routing Algorithms for Fixed Networks / 2.5：
Artificial Intelligence Community / 2.5.1：
Networking Community / 2.5.2：
Summary / 2.6：
From The Wisdom of the Hive to Routing in Telecommunication Networks / 3：
An Agent-Based Investigation of a Honeybee Colony / 3.1：
Labor Management / 3.2.1：
The Communication Network of a Honeybee Colony / 3.2.2：
Reinforcement Learning / 3.2.3：
Distributed Coordination and Planning / 3.2.4：
Energy-Efficient Foraging / 3.2.5：
Stochastic Selection of Flower Sites / 3.2.6：
Group Organization / 3.2.7：
BeeHive: The Mapping of Concepts from Nature to Networks / 3.3：
The Bee Agent Model / 3.4：
Estimation Model of Agents / 3.4.1：
Goodness of a Neighbor / 3.4.2：
Communication Paradigm of Agents / 3.4.3：
Packet-Switching Algorithm / 3.4.4：
BeeHive Algorithm / 3.5：
The Performance Evaluation Framework for Nature-Inspired Routing Algorithms / 3.6：
Routing Algorithms Used for Comparison / 3.7：
OSPF / 3.7.1：
Daemon / 3.7.4：
Simulation Environment for BeeHive / 3.8：
simpleNet / 3.8.1：
NTTNet / 3.8.2：
Node150 / 3.8.3：
Discussion of the Results from the Experiments / 3.9：
Congestion Avoidance Behavior / 3.9.1：
Queue Management Behavior / 3.9.2：
Hot Spots / 3.9.3：
Router Crash Experiments / 3.9.4：
Bursty Traffic Generator / 3.9.5：
Sessionless Network Traffic / 3.9.6：
Size of Routing Table / 3.9.7：
A Scalability Framework for Nature-Inspired Routing Algorithms / 3.10：
Existing Work on Scalability Analysis / 4.1：
The Scalability Model for a Routing Algorithm / 4.1.2：
Cost Model / 4.2.1：
Power Model of an Algorithm / 4.2.2：
Scalability Metric for a Routing Algorithm / 4.2.3：
Simulation Environment for Scalability Analysis / 4.3：
Node350 / 4.3.1：
Node650 / 4.3.5：
Node1050 / 4.3.6：
Throughput and Packet Delivery Ratio / 4.4：
Packet Delay / 4.4.2：
Control Overhead and Suboptimal Overhead / 4.4.3：
Agent and Packet Processing Complexity / 4.4.4：
Routing Table Size / 4.4.5：
Investigation of the Behavior of AntNet / 4.4.6：
Towards an Empirically Founded Scalability Model for Routing Protocols / 4.5：
Scalability Matrix and Scalability Analysis / 4.5.1：
Scalability Analysis of BeeHive / 4.5.2：
Scalability Analysis of AntNet / 4.5.3：
Scalability Analysis of OSPF / 4.5.4：
BeeHive in Real Networks of Linux Routers / 4.6：
Engineering of Nature-Inspired Routing Protocols / 5.1：
Structural Design of a Routing Framework / 5.2.1：
Structural Semantics of the Network Stack / 5.2.2：
System Design Issues / 5.2.3：
Natural Routing Framework: Design and Implementation / 5.3：
Algorithm-Independent Framework / 5.3.1：
Algorithmic-Dependent BeeHive Module / 5.3.2：
Protocol Verification Framework / 5.4：
The Motivation Behind the Design and Structure of Experiments / 5.5：
Quantum Traffic Engineering / 5.6：
Real-World Applications Traffic Engineering / 5.6.2：
Hybrid Traffic Engineering / 5.6.3：
A Formal Framework for Analyzing the Behavior of BeeHive / 5.7：
Goodness / 6.1：
Analytical Model / 6.3：
Node Traffic / 6.3.1：
Link Flows / 6.3.2：
Calculation of Delays / 6.3.3：
Throughput / 6.3.4：
Empirical Verification of the Formal Model / 6.4：
Example 1 / 6.4.1：
Example 2 / 6.4.2：
An Efficient Nature-Inspired Security Framework for BeeHive / 6.5：
Robustness and Security Analysis of a Routing Protocol / 7.1：
Security Threats to Nature-Inspired Routing Protocols / 7.2.1：
Existing Works on Security of Routing Protocols / 7.2.2：
BeeHiveGuard: A Digital Signature-Based Security Framework / 7.3：
Agent Integrity / 7.3.1：
Routing Information Integrity / 7.3.2：
Architecture of BeeHiveGuard / 7.3.3：
BeeHiveAIS: an Immune-Inspired Security Framework for BeeHive / 7.4：
Artificial Immune Systems (AISs) / 7.4.1：
Behavioral Analysis of BeeHive for Designing an AIS / 7.4.2：
The AIS Model of BeeHiveAIS / 7.4.3：
Top-Level BeeHiveAIS / 7.4.4：
Simulation Models of Our Security Frameworks / 7.5：
Attack Scenarios on Simple Topologies / 7.5.1：
Analysis of Attacks and Effectiveness of Security Frameworks / 7.5.2：
Bee-Inspired Routing Protocols for Mobile Ad Hoc and Sensor Networks / 7.5.3：
Existing Works on Nature-Inspired MANET Routing Protocols / 8.1：
Bee Agent Model / 8.1.2：
Packers / 8.2.1：
Scouts / 8.2.2：
Foragers / 8.2.3：
Beeswarm / 8.2.4：
Architecture of BeeAdHoc / 8.3：
Packing Floor / 8.3.1：
Entrance / 8.3.2：
Dance Floor / 8.3.3：
Simulation Framework / 8.4：
Metrics / 8.4.1：
Node Mobility Behavior / 8.4.2：
BeeAdHoc in Real-World MANETs / 8.5：
A Performance Evaluation Framework for Real MANETs in Linux / 8.5.1：
Results of Experiments / 8.6：
Security Threats in BeeAdHoc / 8.7：
Challenges for Routing Protocols in Ad Hoc Sensor Networks / 8.8：
Existing Works on Routing Protocols for Wireless Sensor Networks / 8.8.1：
BeeSensor: Architecture and Working / 8.9：
BeeSensor Agent's Model / 8.9.1：
Protocol Description / 8.9.2：
A Performance Evaluation Framework for Nature-Inspired Routing Protocols for WSNs / 8.10：
Results / 8.10.1：
Conclusion and Future Work / 8.12：
Conclusion / 9.1：
Future Research / 9.2：
Quality of Service (QoS) Routing / 9.2.1：
Cyclic Paths / 9.2.2：
Intelligent and Knowledgeable Network Engineering / 9.2.3：
Bee Colony Metaheuristic / 9.2.4：
Natural Engineering: The Need for a Distinct Discipline / 9.3：
References
Index
Introduction / 1：
Motivation of the Work / 1.1：
Problem Statement / 1.2：
30.

電子ブック
ＥＢ
30. Bee-Inspired Protocol Engineering
Muddassar Farooq, Th Bäck, A. E. Eiben, G. Rozenberg
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2009
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1：
Motivation of the Work / 1.1：
Problem Statement / 1.2：
Hypotheses / 1.2.1：
An Engineering Approach to Nature-Inspired Routing Protocols / 1.3：
The Scientific Contributions of the Work / 1.4：
A Simple, Disributed, Decentralized Multi-Agent System / 1.4.1：
A Comprehensive Routing System / 1.4.2：
An Empirical Comprehensive Performance Evaluation Framework / 1.4.3：
A Scalability Framework for (Nature-Inspired) Agent-Based Routing Protocols / 1.4.4：
Protocol Engineering of Nature-Inspired Routing Protocols / 1.4.5：
A Nature-Inspired Linux Router / 1.4.6：
The Protocol Validation Framework / 1.4.7：
The Formal Framework for Nature-Inspired Protocols / 1.4.8：
A Simple, Efficient, and Scalable Nature-Inspired Security Framework / 1.4.9：
Emerging Mobile and Wireless Sensors Ad Hoc Networks / 1.4.10：
Organization of the Book / 1.5：
A Comprehensive Survey of Nature-Inspired Routing Protocols / 2：
Organization of the Chapter / 2.1：
Network Routing Algorithms / 2.2：
Features Landscape of a Modern Routing Algorithm / 2.2.1：
Taxonomy of Routing Algorithms / 2.2.2：
Ant Colony Optimization (ACO) Routing Algorithms for Fixed Networks / 2.3：
Important Elements of ACO in Routing / 2.3.1：
Ant-Based Control (ABC) for Circuit-Switched Networks / 2.3.2：
Ant-Based Control (ABC) for Packet-Switched Networks / 2.3.3：
AntNet / 2.3.4：
Ant Colony Routing (ACR) and AntNet+SELA QoS-Aware Routing / 2.3.5：
A Brief History of Research in AntNet / 2.3.6：
Evolutionary Routing Algorithms for Fixed Networks / 2.4：
Important Elements of EA in Routing / 2.4.1：
GARA / 2.4.2：
ASGA and SynthECA / 2.4.3：
DGA / 2.4.4：
Related Work on Routing Algorithms for Fixed Networks / 2.5：
Artificial Intelligence Community / 2.5.1：
Networking Community / 2.5.2：
Summary / 2.6：
From The Wisdom of the Hive to Routing in Telecommunication Networks / 3：
An Agent-Based Investigation of a Honeybee Colony / 3.1：
Labor Management / 3.2.1：
The Communication Network of a Honeybee Colony / 3.2.2：
Reinforcement Learning / 3.2.3：
Distributed Coordination and Planning / 3.2.4：
Energy-Efficient Foraging / 3.2.5：
Stochastic Selection of Flower Sites / 3.2.6：
Group Organization / 3.2.7：
BeeHive: The Mapping of Concepts from Nature to Networks / 3.3：
The Bee Agent Model / 3.4：
Estimation Model of Agents / 3.4.1：
Goodness of a Neighbor / 3.4.2：
Communication Paradigm of Agents / 3.4.3：
Packet-Switching Algorithm / 3.4.4：
BeeHive Algorithm / 3.5：
The Performance Evaluation Framework for Nature-Inspired Routing Algorithms / 3.6：
Routing Algorithms Used for Comparison / 3.7：
OSPF / 3.7.1：
Daemon / 3.7.4：
Simulation Environment for BeeHive / 3.8：
simpleNet / 3.8.1：
NTTNet / 3.8.2：
Node150 / 3.8.3：
Discussion of the Results from the Experiments / 3.9：
Congestion Avoidance Behavior / 3.9.1：
Queue Management Behavior / 3.9.2：
Hot Spots / 3.9.3：
Router Crash Experiments / 3.9.4：
Bursty Traffic Generator / 3.9.5：
Sessionless Network Traffic / 3.9.6：
Size of Routing Table / 3.9.7：
A Scalability Framework for Nature-Inspired Routing Algorithms / 3.10：
Existing Work on Scalability Analysis / 4.1：
The Scalability Model for a Routing Algorithm / 4.1.2：
Cost Model / 4.2.1：
Power Model of an Algorithm / 4.2.2：
Scalability Metric for a Routing Algorithm / 4.2.3：
Simulation Environment for Scalability Analysis / 4.3：
Node350 / 4.3.1：
Node650 / 4.3.5：
Node1050 / 4.3.6：
Throughput and Packet Delivery Ratio / 4.4：
Packet Delay / 4.4.2：
Control Overhead and Suboptimal Overhead / 4.4.3：
Agent and Packet Processing Complexity / 4.4.4：
Routing Table Size / 4.4.5：
Investigation of the Behavior of AntNet / 4.4.6：
Towards an Empirically Founded Scalability Model for Routing Protocols / 4.5：
Scalability Matrix and Scalability Analysis / 4.5.1：
Scalability Analysis of BeeHive / 4.5.2：
Scalability Analysis of AntNet / 4.5.3：
Scalability Analysis of OSPF / 4.5.4：
BeeHive in Real Networks of Linux Routers / 4.6：
Engineering of Nature-Inspired Routing Protocols / 5.1：
Structural Design of a Routing Framework / 5.2.1：
Structural Semantics of the Network Stack / 5.2.2：
System Design Issues / 5.2.3：
Natural Routing Framework: Design and Implementation / 5.3：
Algorithm-Independent Framework / 5.3.1：
Algorithmic-Dependent BeeHive Module / 5.3.2：
Protocol Verification Framework / 5.4：
The Motivation Behind the Design and Structure of Experiments / 5.5：
Quantum Traffic Engineering / 5.6：
Real-World Applications Traffic Engineering / 5.6.2：
Hybrid Traffic Engineering / 5.6.3：
A Formal Framework for Analyzing the Behavior of BeeHive / 5.7：
Goodness / 6.1：
Analytical Model / 6.3：
Node Traffic / 6.3.1：
Link Flows / 6.3.2：
Calculation of Delays / 6.3.3：
Throughput / 6.3.4：
Empirical Verification of the Formal Model / 6.4：
Example 1 / 6.4.1：
Example 2 / 6.4.2：
An Efficient Nature-Inspired Security Framework for BeeHive / 6.5：
Robustness and Security Analysis of a Routing Protocol / 7.1：
Security Threats to Nature-Inspired Routing Protocols / 7.2.1：
Existing Works on Security of Routing Protocols / 7.2.2：
BeeHiveGuard: A Digital Signature-Based Security Framework / 7.3：
Agent Integrity / 7.3.1：
Routing Information Integrity / 7.3.2：
Architecture of BeeHiveGuard / 7.3.3：
BeeHiveAIS: an Immune-Inspired Security Framework for BeeHive / 7.4：
Artificial Immune Systems (AISs) / 7.4.1：
Behavioral Analysis of BeeHive for Designing an AIS / 7.4.2：
The AIS Model of BeeHiveAIS / 7.4.3：
Top-Level BeeHiveAIS / 7.4.4：
Simulation Models of Our Security Frameworks / 7.5：
Attack Scenarios on Simple Topologies / 7.5.1：
Analysis of Attacks and Effectiveness of Security Frameworks / 7.5.2：
Bee-Inspired Routing Protocols for Mobile Ad Hoc and Sensor Networks / 7.5.3：
Existing Works on Nature-Inspired MANET Routing Protocols / 8.1：
Bee Agent Model / 8.1.2：
Packers / 8.2.1：
Scouts / 8.2.2：
Foragers / 8.2.3：
Beeswarm / 8.2.4：
Architecture of BeeAdHoc / 8.3：
Packing Floor / 8.3.1：
Entrance / 8.3.2：
Dance Floor / 8.3.3：
Simulation Framework / 8.4：
Metrics / 8.4.1：
Node Mobility Behavior / 8.4.2：
BeeAdHoc in Real-World MANETs / 8.5：
A Performance Evaluation Framework for Real MANETs in Linux / 8.5.1：
Results of Experiments / 8.6：
Security Threats in BeeAdHoc / 8.7：
Challenges for Routing Protocols in Ad Hoc Sensor Networks / 8.8：
Existing Works on Routing Protocols for Wireless Sensor Networks / 8.8.1：
BeeSensor: Architecture and Working / 8.9：
BeeSensor Agent's Model / 8.9.1：
Protocol Description / 8.9.2：
A Performance Evaluation Framework for Nature-Inspired Routing Protocols for WSNs / 8.10：
Results / 8.10.1：
Conclusion and Future Work / 8.12：
Conclusion / 9.1：
Future Research / 9.2：
Quality of Service (QoS) Routing / 9.2.1：
Cyclic Paths / 9.2.2：
Intelligent and Knowledgeable Network Engineering / 9.2.3：
Bee Colony Metaheuristic / 9.2.4：
Natural Engineering: The Need for a Distinct Discipline / 9.3：
References
Index
Introduction / 1：
Motivation of the Work / 1.1：
Problem Statement / 1.2：
31.

電子ブック
ＥＢ
31. Robots, Reasoning, and Reification
James P. Gunderson, Louise F. Gunderson, Unspecified
出版情報: Springer eBooks Computer Science , Springer US, 2009
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Introduction / 1：
Bridging the Gap / 1.1：
Bidirectional Mapping / 1.1.1：
Reification and Preafference in Biological Entities / 1.2：
More Advanced Brains / 1.3：
What This Book Is and What It Is Not / 1.4：
Structure of the Book / 1.5：
A Note on Typefaces and Terminology / 1.6：
Anthropomorphization / 1.6.1：
Some background material on probability and biology / 2：
Layout / 2.1：
Probability in the Real World / 2.2：
Why a Biologically Principled Argument? / 2.3：
Biological Principles / 2.3.1：
What Is a Biologically Principled Argument? / 2.4：
Biology Is an Observational Science / 2.4.1：
Life Has Structure / 2.4.2：
The Theory of Evolution Explains the Observed Diversity of Life / 2.4.3：
So Why Is Our Model Biologically Principled? / 2.5：
Why Not Just Use Expected Value? / 2.5.1：
Using Cognition and Physiology to Build a Cognitive Model / 3：
Reification in Biological Entities / 3.1：
Recognition / 3.1.1：
Preafference / 3.1.2：
Biological Storage / 3.2：
Explicit Memory / 3.2.1：
Emotion / 3.3：
Emotion as mediator / 3.3.1：
Representation / 4：
Representing Features of the World / 4.1：
Representing Goals / 4.2：
Representing Actions in the World / 4.3：
Enabling Conditions / 4.3.1：
Outcomes / 4.3.2：
Representing Likelihoods / 4.3.3：
Exogenous Events / 4.4：
Perception/Action System / 5：
Robot as Perception/Action System / 5.1：
Robot as Body / 5.1.1：
Robot as Senor / 5.1.2：
Robot as Agent of Change / 5.1.3：
Low Level Control Loop - Procedural Memory / 5.1.4：
System Safety and Routine Actions / 5.1.5：
Examples of Perception/Action Systems / 5.2：
Fred - a simple test robot / 5.2.1：
Basil / 5.2.2：
Summary of Perception/Action Systems / 5.3：
Design of a Reification Engine / 6：
Model Selection Criteria / 6.1：
Judgment Analysis / 6.2：
Designing the Reification Engine / 6.3：
Bridging the Sensor to Symbol Gap / 7：
Supporting Bidirectional Mapping / 7.1：
A Third Approach / 7.1.1：
Reification Architecture / 7.2：
PerCepts and Reification / 7.3：
PerCept Data / 7.3.1：
PerCept Function / 7.3.2：
Mental Model / 7.4：
Current World State / 7.5：
Reification functionality / 7.6：
Initialization / 7.6.1：
Mapping the World onto its Model - Recognition / 7.6.2：
Projecting the Model onto the World - Preafference / 7.6.3：
Updating the Current World State / 7.6.4：
Wrapping Up Reification / 7.7：
Working Memory and the Construction of Personal Experiences / 8：
Transient Memory / 8.1：
Working Memory and the Current World State / 8.1.1：
Internal State / 8.1.2：
Episodic Memory / 8.2：
Emotive Tags / 8.2.1：
Memory Services / 8.3：
Providing Memory Services to the Reification Process / 8.4：
Memory, What Was That Again? / 8.5：
Semantic Memory and the Personal Rough Ontology / 9：
Semantic Memory / 9.1：
What is a Personal Rough Ontology? / 9.1.1：
Building Semantic Memory / 9.2：
Structure of the Ontology / 9.2.1：
The nodes in the multi-graph / 9.2.2：
Relationships, the Edges of the Graph / 9.2.3：
A Note on Representing Probabilities / 9.2.4：
Persistent Storage in the Personal Rough Ontology / 9.3：
Transient versus Persistent Knowledge / 9.4：
Extracting Problems for the Deliberative System / 9.5：
Focusing Attention by Finding Sub-Ontologies / 9.6：
Weighted Transitivity / 9.6.1：
Deliberative System / 10：
Deliberation / 10.1：
Reasoning About the Present / 10.2：
Sense-Symbols from the Reification Engine / 10.2.1：
Symbols from the Ontology / 10.2.2：
Reasoning with WorldSets / 10.2.3：
Choosing the Future / 10.3：
Planning as Search / 10.3.1：
Adapting to Failure / 10.3.2：
Plan Evaluation and Selection / 10.4：
Acquiring Distributions / 10.4.1：
Simulator Fidelity / 10.4.2：
Summary / 10.5：
Putting it All Together / 11：
How it Fits Together / 11.1：
Goals and Environment / 11.2：
Knowledge Sources / 11.3：
Ontological Knowledge / 11.3.1：
Reification Knowledge / 11.3.2：
Perception/Action Knowledge / 11.3.3：
The process / 11.4：
Perception/Action / 11.4.1：
Reification / 11.4.2：
Execution / 11.4.3：
Execution, Reification and Action / 11.4.4：
Perception/Action - Reflex / 11.4.6：
Execution Failure / 11.4.7：
Back Up to Deliberation / 11.4.8：
Procedural Memory and Localization / 11.4.9：
A Few Notes About the General Flow / 11.5：
Testing / 12：
Testing the Robot, or How Does One Test an Embedded System? / 12.1：
eXtreme Programming / 12.2：
Methodology for Testing Embodied Systems / 12.3：
Benefits of Partitioning the Tests / 12.3.1：
General Testing Guidelines / 12.4：
General Partitioning Guidelines / 12.4.1：
Testing in the lab / 12.5：
Hardware / 12.5.1：
Static Tests / 12.5.2：
Dynamic tests / 12.5.3：
Formal System Tests - Testing In The Real World / 12.6：
Testing Recognition / 12.6.1：
Testing Preafference / 12.6.2：
Testing Self-Localization / 12.6.3：
Where do we go from here / 12.7：
A Stopping Point / 13.1：
Next Steps / 13.2：
Adding Learning to the Model / 13.2.1：
Adding Additional Data Sources / 13.2.2：
Porting the Brain into New Bodies / 13.2.3：
Glossary
References
Index
Introduction / 1：
Bridging the Gap / 1.1：
Bidirectional Mapping / 1.1.1：
32.

電子ブック
ＥＢ
32. Understanding Planning Tasks
Malte Helmert, J?rg Siekmann
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2008
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目次情報: 続きを見る
Planning Benchmarks / Part I：
The Role of Benchmarks / 1：
Evaluating Planner Performance / 1.1：
Worst-Case Evaluation / 1.1.1：
Average-Case Evaluation / 1.1.2：
Planning Benchmarks Are Important / 1.2：
Theoretical Analyses of Planning Benchmarks / 1.3：
Why Theoretical Analyses Are Useful / 1.3.1：
Published Results on Benchmark Complexity / 1.3.2：
Standard Benchmarks / 1.4：
Summary and Overview / 1.5：
Defining Planning Domains / 2：
Optimization Problems / 2.1：
Minimization Problems / 2.1.1：
Approximation Algorithms / 2.1.2：
Approximation Classes / 2.1.3：
Reductions / 2.1.4：
Formalizing Planning Domains / 2.2：
General Results and Reductions / 2.3：
Upper Bounds / 2.3.1：
Shortest Plan Length / 2.3.2：
Approximation Classes of Limited Interest / 2.3.3：
Relating Planning and (Bounded) Plan Existence / 2.3.4：
Generalization and Specialization / 2.3.5：
The Benchmark Suite / 3：
Defining the Competition Domains / 3.1：
IPC1 Domains / 3.2：
IPC2 Domains / 3.2.2：
IPC3 Domains / 3.2.3：
IPC4 Domains / 3.2.4：
Domains and Domain Families / 3.3：
Transportation and Route Planning / 4：
Transport and Route / 4.1：
The Transport Domain / 4.1.1：
The Route Domain / 4.1.2：
Special Cases and Hierarchy / 4.1.3：
General Results / 4.2：
Plan Existence / 4.3：
Hardness of Optimization / 4.4：
Constant Factor Approximation / 4.5：
Hardness of Constant Factor Approximation / 4.6：
Summary / 4.7：
Beyond Transport and Route / 4.8：
IPC Domains: Transportation and Route Planning / 5：
Gripper / 5.1：
Mystery and Mystery Prime / 5.2：
Logistics / 5.3：
Zenotravel / 5.4：
Depots / 5.5：
Miconic-10 / 5.6：
Rovers / 5.7：
Grid / 5.8：
Driverlog / 5.9：
Airport / 5.10：
IPC Domains: Others / 5.11：
Assembly / 6.1：
Blocksworld / 6.2：
Freecell / 6.3：
Movie / 6.4：
Pipesworld / 6.5：
Promela / 6.6：
PSR / 6.7：
Satellite / 6.8：
Schedule / 6.9：
Conclusions / 6.10：
Ten Conclusions / 7.1：
Going Further / 7.2：
Fast Downward / Part II：
Solving Planning Tasks Hierarchically / 8：
Introduction / 8.1：
Related Work / 8.2：
Causal Graphs and Abstraction / 8.2.1：
Causal Graphs and Unary STRIPS Operators / 8.2.2：
Multi-Valued Planning Tasks / 8.2.3：
Architecture and Overview / 8.3：
Translation / 9：
PDDL and Multi-valued Planning Tasks / 9.1：
Translation Overview / 9.2：
Normalization / 9.3：
Compiling Away Types / 9.3.1：
Simplifying Conditions / 9.3.2：
Simplifying Effects / 9.3.3：
Normalization Result / 9.3.4：
Invariant Synthesis / 9.4：
Initial Candidates / 9.4.1：
Proving Invariance / 9.4.2：
Refining Failed Candidates / 9.4.3：
Examples / 9.4.4：
Grounding / 9.4.5：
Overview of Horn Exploration / 9.5.1：
Generating the Logic Program / 9.5.2：
Translating the Logic Program to Normal Form / 9.5.3：
Computing the Canonical Model / 9.5.4：
Axiom and Operator Instantiation / 9.5.5：
Multi-valued Planning Task Generation / 9.6：
Variable Selection / 9.6.1：
Converting the Initial State / 9.6.2：
Converting Operator Effects / 9.6.3：
Converting Conditions / 9.6.4：
Computing Axiom Layers / 9.6.5：
Generating the Output / 9.6.6：
Performance Notes / 9.7：
Relative Performance Compared to MIPS Translator / 9.7.1：
Absolute Performance / 9.7.2：
Knowledge Compilation / 10：
Overview / 10.1：
Domain Transition Graphs / 10.2：
Causal Graphs / 10.3：
Acyclic Causal Graphs / 10.3.1：
Generating and Pruning Causal Graphs / 10.3.2：
Causal Graph Examples / 10.3.3：
Successor Generators and Axiom Evaluators / 10.4：
Successor Generators / 10.4.1：
Axiom Evaluators / 10.4.2：
Search / 11：
The Causal Graph Heuristic / 11.1：
Conceptual View of the Causal Graph Heurstic / 11.2.1：
Computation of the Causal Graph Heuristic / 11.2.2：
States with Infinite Heuristic Value / 11.2.3：
Helpful Transitions / 11.2.4：
The FF Heuristic / 11.3：
Greedy Best-First Search in Fast Downward / 11.4：
Preferred Operators / 11.4.1：
Deferred Heuristic Evaluation / 11.4.2：
Multi-heuristic Best-First Search / 11.5：
Focused Iterative-Broadening Search / 11.6：
Experiments / 12：
Experiment Design / 12.1：
Benchmark Set / 12.1.1：
Experiment Setup / 12.1.2：
Translation and Knowledge Compilation vs. Search / 12.1.3：
Strips Domains from IPC1-3 / 12.2：
ADL Domains from IPC1-3 / 12.3：
Domains from IPC4 / 12.4：
Conclusions from the Experiment / 12.5：
Discussion / 13：
Major Contributors / 13.1：
Multi-valued Representations / 13.2.1：
Task Decomposition Heuristics / 13.2.2：
Minor Contributions / 13.3：
References / 13.4：
Index
Planning Benchmarks / Part I：
The Role of Benchmarks / 1：
Evaluating Planner Performance / 1.1：
33.

電子ブック
ＥＢ
33. Robots, Reasoning, and Reification
James P. Gunderson, Louise F. Gunderson, Unspecified
出版情報: SpringerLink Books - AutoHoldings , Springer US, 2009
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Introduction / 1：
Bridging the Gap / 1.1：
Bidirectional Mapping / 1.1.1：
Reification and Preafference in Biological Entities / 1.2：
More Advanced Brains / 1.3：
What This Book Is and What It Is Not / 1.4：
Structure of the Book / 1.5：
A Note on Typefaces and Terminology / 1.6：
Anthropomorphization / 1.6.1：
Some background material on probability and biology / 2：
Layout / 2.1：
Probability in the Real World / 2.2：
Why a Biologically Principled Argument? / 2.3：
Biological Principles / 2.3.1：
What Is a Biologically Principled Argument? / 2.4：
Biology Is an Observational Science / 2.4.1：
Life Has Structure / 2.4.2：
The Theory of Evolution Explains the Observed Diversity of Life / 2.4.3：
So Why Is Our Model Biologically Principled? / 2.5：
Why Not Just Use Expected Value? / 2.5.1：
Using Cognition and Physiology to Build a Cognitive Model / 3：
Reification in Biological Entities / 3.1：
Recognition / 3.1.1：
Preafference / 3.1.2：
Biological Storage / 3.2：
Explicit Memory / 3.2.1：
Emotion / 3.3：
Emotion as mediator / 3.3.1：
Representation / 4：
Representing Features of the World / 4.1：
Representing Goals / 4.2：
Representing Actions in the World / 4.3：
Enabling Conditions / 4.3.1：
Outcomes / 4.3.2：
Representing Likelihoods / 4.3.3：
Exogenous Events / 4.4：
Perception/Action System / 5：
Robot as Perception/Action System / 5.1：
Robot as Body / 5.1.1：
Robot as Senor / 5.1.2：
Robot as Agent of Change / 5.1.3：
Low Level Control Loop - Procedural Memory / 5.1.4：
System Safety and Routine Actions / 5.1.5：
Examples of Perception/Action Systems / 5.2：
Fred - a simple test robot / 5.2.1：
Basil / 5.2.2：
Summary of Perception/Action Systems / 5.3：
Design of a Reification Engine / 6：
Model Selection Criteria / 6.1：
Judgment Analysis / 6.2：
Designing the Reification Engine / 6.3：
Bridging the Sensor to Symbol Gap / 7：
Supporting Bidirectional Mapping / 7.1：
A Third Approach / 7.1.1：
Reification Architecture / 7.2：
PerCepts and Reification / 7.3：
PerCept Data / 7.3.1：
PerCept Function / 7.3.2：
Mental Model / 7.4：
Current World State / 7.5：
Reification functionality / 7.6：
Initialization / 7.6.1：
Mapping the World onto its Model - Recognition / 7.6.2：
Projecting the Model onto the World - Preafference / 7.6.3：
Updating the Current World State / 7.6.4：
Wrapping Up Reification / 7.7：
Working Memory and the Construction of Personal Experiences / 8：
Transient Memory / 8.1：
Working Memory and the Current World State / 8.1.1：
Internal State / 8.1.2：
Episodic Memory / 8.2：
Emotive Tags / 8.2.1：
Memory Services / 8.3：
Providing Memory Services to the Reification Process / 8.4：
Memory, What Was That Again? / 8.5：
Semantic Memory and the Personal Rough Ontology / 9：
Semantic Memory / 9.1：
What is a Personal Rough Ontology? / 9.1.1：
Building Semantic Memory / 9.2：
Structure of the Ontology / 9.2.1：
The nodes in the multi-graph / 9.2.2：
Relationships, the Edges of the Graph / 9.2.3：
A Note on Representing Probabilities / 9.2.4：
Persistent Storage in the Personal Rough Ontology / 9.3：
Transient versus Persistent Knowledge / 9.4：
Extracting Problems for the Deliberative System / 9.5：
Focusing Attention by Finding Sub-Ontologies / 9.6：
Weighted Transitivity / 9.6.1：
Deliberative System / 10：
Deliberation / 10.1：
Reasoning About the Present / 10.2：
Sense-Symbols from the Reification Engine / 10.2.1：
Symbols from the Ontology / 10.2.2：
Reasoning with WorldSets / 10.2.3：
Choosing the Future / 10.3：
Planning as Search / 10.3.1：
Adapting to Failure / 10.3.2：
Plan Evaluation and Selection / 10.4：
Acquiring Distributions / 10.4.1：
Simulator Fidelity / 10.4.2：
Summary / 10.5：
Putting it All Together / 11：
How it Fits Together / 11.1：
Goals and Environment / 11.2：
Knowledge Sources / 11.3：
Ontological Knowledge / 11.3.1：
Reification Knowledge / 11.3.2：
Perception/Action Knowledge / 11.3.3：
The process / 11.4：
Perception/Action / 11.4.1：
Reification / 11.4.2：
Execution / 11.4.3：
Execution, Reification and Action / 11.4.4：
Perception/Action - Reflex / 11.4.6：
Execution Failure / 11.4.7：
Back Up to Deliberation / 11.4.8：
Procedural Memory and Localization / 11.4.9：
A Few Notes About the General Flow / 11.5：
Testing / 12：
Testing the Robot, or How Does One Test an Embedded System? / 12.1：
eXtreme Programming / 12.2：
Methodology for Testing Embodied Systems / 12.3：
Benefits of Partitioning the Tests / 12.3.1：
General Testing Guidelines / 12.4：
General Partitioning Guidelines / 12.4.1：
Testing in the lab / 12.5：
Hardware / 12.5.1：
Static Tests / 12.5.2：
Dynamic tests / 12.5.3：
Formal System Tests - Testing In The Real World / 12.6：
Testing Recognition / 12.6.1：
Testing Preafference / 12.6.2：
Testing Self-Localization / 12.6.3：
Where do we go from here / 12.7：
A Stopping Point / 13.1：
Next Steps / 13.2：
Adding Learning to the Model / 13.2.1：
Adding Additional Data Sources / 13.2.2：
Porting the Brain into New Bodies / 13.2.3：
Glossary
References
Index
Introduction / 1：
Bridging the Gap / 1.1：
Bidirectional Mapping / 1.1.1：
34.

電子ブック
ＥＢ
34. Understanding Planning Tasks
Malte Helmert, Jörg Siekmann
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2008
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Planning Benchmarks / Part I：
The Role of Benchmarks / 1：
Evaluating Planner Performance / 1.1：
Worst-Case Evaluation / 1.1.1：
Average-Case Evaluation / 1.1.2：
Planning Benchmarks Are Important / 1.2：
Theoretical Analyses of Planning Benchmarks / 1.3：
Why Theoretical Analyses Are Useful / 1.3.1：
Published Results on Benchmark Complexity / 1.3.2：
Standard Benchmarks / 1.4：
Summary and Overview / 1.5：
Defining Planning Domains / 2：
Optimization Problems / 2.1：
Minimization Problems / 2.1.1：
Approximation Algorithms / 2.1.2：
Approximation Classes / 2.1.3：
Reductions / 2.1.4：
Formalizing Planning Domains / 2.2：
General Results and Reductions / 2.3：
Upper Bounds / 2.3.1：
Shortest Plan Length / 2.3.2：
Approximation Classes of Limited Interest / 2.3.3：
Relating Planning and (Bounded) Plan Existence / 2.3.4：
Generalization and Specialization / 2.3.5：
The Benchmark Suite / 3：
Defining the Competition Domains / 3.1：
IPC1 Domains / 3.2：
IPC2 Domains / 3.2.2：
IPC3 Domains / 3.2.3：
IPC4 Domains / 3.2.4：
Domains and Domain Families / 3.3：
Transportation and Route Planning / 4：
Transport and Route / 4.1：
The Transport Domain / 4.1.1：
The Route Domain / 4.1.2：
Special Cases and Hierarchy / 4.1.3：
General Results / 4.2：
Plan Existence / 4.3：
Hardness of Optimization / 4.4：
Constant Factor Approximation / 4.5：
Hardness of Constant Factor Approximation / 4.6：
Summary / 4.7：
Beyond Transport and Route / 4.8：
IPC Domains: Transportation and Route Planning / 5：
Gripper / 5.1：
Mystery and Mystery Prime / 5.2：
Logistics / 5.3：
Zenotravel / 5.4：
Depots / 5.5：
Miconic-10 / 5.6：
Rovers / 5.7：
Grid / 5.8：
Driverlog / 5.9：
Airport / 5.10：
IPC Domains: Others / 5.11：
Assembly / 6.1：
Blocksworld / 6.2：
Freecell / 6.3：
Movie / 6.4：
Pipesworld / 6.5：
Promela / 6.6：
PSR / 6.7：
Satellite / 6.8：
Schedule / 6.9：
Conclusions / 6.10：
Ten Conclusions / 7.1：
Going Further / 7.2：
Fast Downward / Part II：
Solving Planning Tasks Hierarchically / 8：
Introduction / 8.1：
Related Work / 8.2：
Causal Graphs and Abstraction / 8.2.1：
Causal Graphs and Unary STRIPS Operators / 8.2.2：
Multi-Valued Planning Tasks / 8.2.3：
Architecture and Overview / 8.3：
Translation / 9：
PDDL and Multi-valued Planning Tasks / 9.1：
Translation Overview / 9.2：
Normalization / 9.3：
Compiling Away Types / 9.3.1：
Simplifying Conditions / 9.3.2：
Simplifying Effects / 9.3.3：
Normalization Result / 9.3.4：
Invariant Synthesis / 9.4：
Initial Candidates / 9.4.1：
Proving Invariance / 9.4.2：
Refining Failed Candidates / 9.4.3：
Examples / 9.4.4：
Grounding / 9.4.5：
Overview of Horn Exploration / 9.5.1：
Generating the Logic Program / 9.5.2：
Translating the Logic Program to Normal Form / 9.5.3：
Computing the Canonical Model / 9.5.4：
Axiom and Operator Instantiation / 9.5.5：
Multi-valued Planning Task Generation / 9.6：
Variable Selection / 9.6.1：
Converting the Initial State / 9.6.2：
Converting Operator Effects / 9.6.3：
Converting Conditions / 9.6.4：
Computing Axiom Layers / 9.6.5：
Generating the Output / 9.6.6：
Performance Notes / 9.7：
Relative Performance Compared to MIPS Translator / 9.7.1：
Absolute Performance / 9.7.2：
Knowledge Compilation / 10：
Overview / 10.1：
Domain Transition Graphs / 10.2：
Causal Graphs / 10.3：
Acyclic Causal Graphs / 10.3.1：
Generating and Pruning Causal Graphs / 10.3.2：
Causal Graph Examples / 10.3.3：
Successor Generators and Axiom Evaluators / 10.4：
Successor Generators / 10.4.1：
Axiom Evaluators / 10.4.2：
Search / 11：
The Causal Graph Heuristic / 11.1：
Conceptual View of the Causal Graph Heurstic / 11.2.1：
Computation of the Causal Graph Heuristic / 11.2.2：
States with Infinite Heuristic Value / 11.2.3：
Helpful Transitions / 11.2.4：
The FF Heuristic / 11.3：
Greedy Best-First Search in Fast Downward / 11.4：
Preferred Operators / 11.4.1：
Deferred Heuristic Evaluation / 11.4.2：
Multi-heuristic Best-First Search / 11.5：
Focused Iterative-Broadening Search / 11.6：
Experiments / 12：
Experiment Design / 12.1：
Benchmark Set / 12.1.1：
Experiment Setup / 12.1.2：
Translation and Knowledge Compilation vs. Search / 12.1.3：
Strips Domains from IPC1-3 / 12.2：
ADL Domains from IPC1-3 / 12.3：
Domains from IPC4 / 12.4：
Conclusions from the Experiment / 12.5：
Discussion / 13：
Major Contributors / 13.1：
Multi-valued Representations / 13.2.1：
Task Decomposition Heuristics / 13.2.2：
Minor Contributions / 13.3：
References / 13.4：
Index
Planning Benchmarks / Part I：
The Role of Benchmarks / 1：
Evaluating Planner Performance / 1.1：
35.

電子ブック
ＥＢ
35. Introduction to Artificial Intelligence
Wolfgang Ertel
出版情報: Springer eBooks Computer Science , Springer London, 2011
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Introduction / 1：
What Is Artificial Intelligence? / 1.1：
Brain Science and Problem Solving / 1.1.1：
The Turing Test and Chatterbots / 1.1.2：
The History of AI / 1.2：
The First Beginnings / 1.2.1：
Logic Solves (Almost) All Problems / 1.2.2：
The New Connection ism / 1.2.3：
Reasoning Under Uncertainty / 1.2.4：
Distributed, Autonomous and Learning Agents / 1.2.5：
AI Grows up / 1.2.6：
Agents / 1.3：
Knowledge-Based Systems / 1.4：
Exercises / 1.5：
Propositional Logic / 2：
Syntax / 2.1：
Semantics / 2.2：
Proof Systems / 2.3：
Resolution / 2.4：
Horn Clauses / 2.5：
Computability and Complexity / 2.6：
Applications and Limitations / 2.7：
First-order Predicate Logic / 2.8：
Equality / 3.1：
Quantifiers and Normal Forms / 3.3：
ProofCalculi / 3.4：
Resolution Strategies / 3.5：
Automated Theorem Provers / 3.5.2：
Mathematical Examples / 3.7：
Applications / 3.8：
Summary / 3.9：
Limitations of Logic / 3.10：
The Search Space Problem / 4.1：
Decidability and Incompleteness / 4.2：
The Flying Penguin / 4.3：
Modeling Uncertainty / 4.4：
Logic Programming with Prolog / 4.5：
Prolog Systems and Implementations / 5.1：
Simple Examples / 5.2：
Execution Control and Procedural Elements / 5.3：
Lists / 5.4：
Self-modifying Programs / 5.5：
A Planning Example / 5.6：
Constraint Logic Programming / 5.7：
Search, Games and Problem Solving / 5.8：
Uninformed Search / 6.1：
Breadth-First Search / 6.2.1：
Depth-First Search / 6.2.2：
Iterative Deepening / 6.2.3：
Comparison / 6.2.4：
Heuristic Search / 6.3：
Greedy Search / 6.3.1：
A-Search / 6.3.2：
Ida-Search / 6.3.3：
Empirical Comparison of the Search Algorithms / 6.3.4：
Games with Opponents / 6.3.5：
Minimax Search / 6.4.1：
Alpha-Beta-Pruning / 6.4.2：
Non-deterministic Games / 6.4.3：
Heuristic Evaluation Functions / 6.5：
Learning of Heuristics / 6.5.1：
State of the Art / 6.6：
Reasoning with Uncertainty / 6.7：
Computing with Probabilities / 7.1：
Conditional Probability / 7.1.1：
The Principle of Maximum Entropy / 7.2：
An Inference Rule for Probabilities / 7.2.1：
Maximum Entropy Without Explicit Constraints / 7.2.2：
Conditional Probability Versus Material Implication / 7.2.3：
MaxEnt-Systems / 7.2.4：
The Tweety Example / 7.2.5：
Lexmed, an Expert System for Diagnosing Appendicitis / 7.3：
Appendicitis Diagnosis with Formal Methods / 7.3.1：
Hybrid Probabilistic Knowledge Base / 7.3.2：
Application of Lexmed / 7.3.3：
Function of Lexmed / 7.3.4：
Risk Management Using the Cost Matrix / 7.3.5：
Performance / 7.3.6：
Application Areas and Experiences / 7.3.7：
Reasoning with Bayesian Networks / 7.4：
Independent Variables / 7.4.1：
Graphical Representation of Knowledge as a Bayesian Network / 7.4.2：
Conditional Independence / 7.4.3：
Practical Application / 7.4.4：
Software for Bayesian Networks / 7.4.5：
Development of Bayesian Networks / 7.4.6：
Semantics of Bayesian Networks / 7.4.7：
Machine Learning and Data Mining / 7.5：
Data Analysis / 8.1：
The Perceptron, a Linear Classifier / 8.2：
The Learning Rule / 8.2.1：
Optimization and Outlook / 8.2.2：
The Nearest Neighbor Method / 8.3：
Two Classes, Many Classes, Approximation / 8.3.1：
Distance Is Relevant / 8.3.2：
Computation Times / 8.3.3：
Summary and Outlook / 8.3.4：
Case-Based Reasoning / 8.3.5：
Decision Tree Learning / 8.4：
A Simple Example / 8.4.1：
Entropy as a Metric for Information Content / 8.4.2：
Information Gain / 8.4.3：
Application of C4.5 / 8.4.4：
Learning of Appendicitis Diagnosis / 8.4.5：
Continuous Attributes / 8.4.6：
PruningùCutting the Tree / 8.4.7：
Missing Values / 8.4.8：
Learning of Bayesian Networks / 8.4.9：
Learning the Network Structure / 8.5.l：
The Naive Bayes Classifier / 8:6：
Text Classification with Naive Bayes / 8.6.1：
Clustering / 8.7：
Distance Metrics / 8.7.1：
k-Means and the Em Algorithm / 8.7.2：
Hierarchical Clustering / 8.7.3：
Data Mining in Practice / 8.8：
The Data Mining Tool Knime / 8.8.1：
The Perceptron / 8.9：
Nearest Neighbor Method / 8.10.3：
Decision Trees / 8.10.4：
Data Mining / 8.10.5：
Neural Networks / 9：
From Biology to Simulation / 9.1：
The Mathematical Model / 9.1.1：
Hopfield Networks / 9.2：
Application to a Pattern Recognition Example / 9.2.1：
Analysis / 9.2.2：
Neural Associative Memory / 9.2.3：
Correlation Matrix Memory / 9.3.1：
The Pseudoinverse / 9.3.2：
The Binary Hebb Rule / 9.3.3：
A Spelling Correction Program / 9.3.4：
Linear Networks with Minimal Errors / 9.4：
Least Squares Method / 9.4.1：
Application to the Appendicitis Data / 9.4.2：
The Delta Rule / 9.4.3：
Comparison to the Perceptron / 9.4.4：
The Backpropagation Algorithm / 9.5：
Nettalk: A Network Learns to Speak / 9.5.1：
Learning of Heuristics for Theorem Provers / 9.5.2：
Problems and Improvements / 9.5.3：
Support Vector Machines / 9.6：
Backpropagation / 9.7：
Reinforcement Learning / 9.9.5：
The Task / 10.1：
Uninformed Combinatorial Search / 10.3：
Value Iteration and Dynamic Programming / 10.4：
A Learning Walking Robot and Its Simulation / 10.5：
Q-Learning / 10.6：
Q-Learning in a Nondeterministic Environment / 10.6.1：
Exploration and Exploitation / 10.7：
Approximation, Generalization and Convergence / 10.8：
Curse of Dimensionality / 10.9：
Solutions for the Exercises / 10.11：
First-Order Predicate Logic / 11.1：
Prolog / 11.4：
References / 11.6：
Index
Introduction / 1：
What Is Artificial Intelligence? / 1.1：
Brain Science and Problem Solving / 1.1.1：
36.

電子ブック
ＥＢ
36. Case-Based Approximate Reasoning
Eyke H?llermeier, Eyke H?llermeier
出版情報: Springer eBooks Computer Science , Springer Netherlands, 2007
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Dedication
Foreword
Preface
Notation
Introduction / 1：
Similarity and case-based reasoning / 1.1：
Objective of this book / 1.2：
Making case-based inference more reliable / 1.2.1：
The important role of models / 1.2.2：
Formal models of case-based inference / 1.2.3：
Overview / 1.3：
Similarity and Case-Based Inference / 2：
Model-based and instance-based approaches / 2.1：
Model-based approaches / 2.1.1：
Instance-based approaches / 2.1.2：
Knowledge representation / 2.1.3：
Performance in generalization / 2.1.4：
Computational complexity / 2.1.5：
Similarity-based methods / 2.2：
Nearest neighbor (NN) estimation / 2.2.1：
Instance-based learning / 2.2.2：
Case-based reasoning / 2.2.3：
The concept of similarity / 2.3：
Similarity in case-based reasoning / 2.3.1：
Similarity and fuzzy sets / 2.3.2：
Aggregation of local similarity measures / 2.3.3：
Case-based inference / 2.4：
Deterministic inference problems / 2.4.1：
Non-deterministic inference problems / 2.4.2：
Summary and remarks / 2.4.3：
Constraint-Based Modeling of Case-Based Inference / 3：
Basic concepts / 3.1：
Similarity profiles and hypotheses / 3.1.1：
Generalized similarity profiles / 3.1.2：
Constraint-based inference / 3.2：
A constraint-based inference scheme / 3.2.1：
Non-deterministic problems / 3.2.2：
Case-based approximation / 3.3：
Properties of case-based approximation / 3.3.1：
Local similarity profiles / 3.3.2：
Learning similarity hypotheses / 3.4：
The learning task / 3.4.1：
A learning algorithm / 3.4.2：
Properties of case-based learning / 3.4.3：
Experimental results / 3.4.4：
Application to statistical inference / 3.5：
Case-based parameter estimation / 3.5.1：
Case-based prior elicitation / 3.5.2：
Probabilistic Modeling of Case-Based Inference / 3.6：
Basic probabilistic concepts / 4.1：
Probabilistic similarity profiles and hypotheses / 4.1.1：
Generalized probabilistic profiles / 4.1.2：
Case-based inference, probabilistic reasoning, and statistical inference / 4.2：
Learning probabilistic similarity hypotheses / 4.3：
Simple hypotheses and credible case-based inference / 4.3.1：
Extended case-based learning / 4.3.2：
Experiments with regression and label ranking / 4.4：
Regression: artificial data / 4.4.1：
Regression: real-world data / 4.4.2：
Label ranking / 4.4.3：
Case-based inference as evidential reasoning / 4.5：
Transformation of probabilistic evidence / 4.5.1：
Inference from individual cases / 4.5.2：
Combining evidence from several cases / 4.5.3：
Assessment of cases / 4.6：
Similarity-weighted approximation / 4.6.1：
More general criteria / 4.6.2：
Assessment of individual cases / 4.6.3：
Complex similarity hypotheses / 4.7：
Inference schemes of higher order / 4.7.1：
Partially admissible profiles / 4.7.2：
Approximate probabilistic inference / 4.8：
Generalized uncertainty measures and profiles / 4.8.1：
An approximate inference scheme / 4.8.2：
Fuzzy Set-Based Modeling of Case-Based Inference I / 4.9：
Background on possibility theory / 5.1：
Possibility distributions as generalized constraints / 5.1.1：
Possibility as evidential support / 5.1.2：
Fuzzy rule-based modeling of the CBI hypothesis / 5.2：
Possibility rules / 5.2.1：
Modeling the CBI hypothesis / 5.2.2：
Generalized possibilistic prediction / 5.3：
Control of compensation and accumulation of support / 5.3.1：
Possibilistic support and weighted NN estimation / 5.3.2：
Upper and lower possibility bounds / 5.3.3：
Fuzzy logical evaluation / 5.3.4：
Comparison of extrapolation principles / 5.3.5：
From predictions to decisions / 5.3.6：
An illustrative example / 5.3.7：
Complexity issues / 5.3.8：
Extensions of the basic model / 5.4：
Dealing with incomplete information / 5.4.1：
Discounting noisy and atypical instances / 5.4.2：
From instances to rules / 5.4.3：
Modified possibility rules / 5.4.4：
Combination of several rules / 5.4.5：
Locally restricted extrapolation / 5.4.6：
Incorporation of background knowledge / 5.4.7：
Experimental studies / 5.5：
Preliminaries / 5.5.1：
Classification accuracy / 5.5.2：
Statistical assumptions and robustness / 5.5.3：
Variation of the aggregation operator / 5.5.4：
Representation of uncertainty / 5.5.5：
Calibration of CBI models / 5.6：
Relations to other fields / 5.7：
Fuzzy and possibilistic data analysis / 5.7.1：
Fuzzy set-based approximate reasoning / 5.7.2：
Fuzzy Set-Based Modeling of Case-Based Inference II / 5.8：
Gradual inference rules / 6.1：
The basic model / 6.1.1：
Modification of gradual rules / 6.1.2：
Certainty rules / 6.2：
Cases as information sources / 6.3：
A probabilistic model / 6.3.1：
Combination of information sources / 6.3.2：
Exceptionality and assessment of cases / 6.4：
Local rules / 6.5：
Case-Based Decision Making / 6.6：
Case-based decision theory / 7.1：
Nearest Neighbor decisions / 7.2：
Nearest Neighbor classification and decision making / 7.2.1：
Nearest Neighbor decision rules / 7.2.2：
An axiomatic characterization / 7.2.3：
Fuzzy modeling of case-based decisions / 7.3：
Basic measures for act evaluation / 7.3.1：
Modification of the basic measures / 7.3.2：
Interpretation of the decision criteria / 7.3.3：
Fuzzy quantification in act evaluation / 7.4：
A CBI framework of CBDM / 7.5：
Generalized decision-theoretic setups / 7.5.1：
Decision making using belief functions / 7.5.2：
Possibilistic decision making / 7.5.3：
CBDM models: A discussion of selected issues / 7.6：
The relation between similarity, preference, and belief / 7.6.1：
The effect of observed cases / 7.6.2：
Dynamic aspects of decision making / 7.6.3：
Experience-based decision making / 7.7：
Compiled decision models / 7.7.1：
Satisficing decision trees / 7.7.2：
Experimental evaluation / 7.7.3：
Conclusions and Outlook / 7.8：
Possibilistic Dominance in Qualitative Decisions / A：
Implication-Based Fuzzy Rules as Randomized Gradual Rules / B：
Implication-based fuzzy rules / B.1：
Gradual rules / B.1.1：
Other implication-based rules / B.1.2：
Randomized gradual rules / B.2：
A probabilistic representation of implication-based fuzzy rules / B.3：
Similarity-Based Reasoning as Logical Inference / C：
Simulation Results of Section 3.4.4 / D：
Experimental Results of Section 5.5.4 / E：
Simulation Results of Section 7.4 / F：
Computation of an Extended Splitting Measures / G：
Experimental Results of Section 7.7.2 / H：
References
Index
Dedication
Foreword
Preface
37.

電子ブック
ＥＢ
37. Case-Based Approximate Reasoning
Eyke Hüllermeier, Eyke Hüllermeier
出版情報: SpringerLink Books - AutoHoldings , Springer Netherlands, 2007
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Dedication
Foreword
Preface
Notation
Introduction / 1：
Similarity and case-based reasoning / 1.1：
Objective of this book / 1.2：
Making case-based inference more reliable / 1.2.1：
The important role of models / 1.2.2：
Formal models of case-based inference / 1.2.3：
Overview / 1.3：
Similarity and Case-Based Inference / 2：
Model-based and instance-based approaches / 2.1：
Model-based approaches / 2.1.1：
Instance-based approaches / 2.1.2：
Knowledge representation / 2.1.3：
Performance in generalization / 2.1.4：
Computational complexity / 2.1.5：
Similarity-based methods / 2.2：
Nearest neighbor (NN) estimation / 2.2.1：
Instance-based learning / 2.2.2：
Case-based reasoning / 2.2.3：
The concept of similarity / 2.3：
Similarity in case-based reasoning / 2.3.1：
Similarity and fuzzy sets / 2.3.2：
Aggregation of local similarity measures / 2.3.3：
Case-based inference / 2.4：
Deterministic inference problems / 2.4.1：
Non-deterministic inference problems / 2.4.2：
Summary and remarks / 2.4.3：
Constraint-Based Modeling of Case-Based Inference / 3：
Basic concepts / 3.1：
Similarity profiles and hypotheses / 3.1.1：
Generalized similarity profiles / 3.1.2：
Constraint-based inference / 3.2：
A constraint-based inference scheme / 3.2.1：
Non-deterministic problems / 3.2.2：
Case-based approximation / 3.3：
Properties of case-based approximation / 3.3.1：
Local similarity profiles / 3.3.2：
Learning similarity hypotheses / 3.4：
The learning task / 3.4.1：
A learning algorithm / 3.4.2：
Properties of case-based learning / 3.4.3：
Experimental results / 3.4.4：
Application to statistical inference / 3.5：
Case-based parameter estimation / 3.5.1：
Case-based prior elicitation / 3.5.2：
Probabilistic Modeling of Case-Based Inference / 3.6：
Basic probabilistic concepts / 4.1：
Probabilistic similarity profiles and hypotheses / 4.1.1：
Generalized probabilistic profiles / 4.1.2：
Case-based inference, probabilistic reasoning, and statistical inference / 4.2：
Learning probabilistic similarity hypotheses / 4.3：
Simple hypotheses and credible case-based inference / 4.3.1：
Extended case-based learning / 4.3.2：
Experiments with regression and label ranking / 4.4：
Regression: artificial data / 4.4.1：
Regression: real-world data / 4.4.2：
Label ranking / 4.4.3：
Case-based inference as evidential reasoning / 4.5：
Transformation of probabilistic evidence / 4.5.1：
Inference from individual cases / 4.5.2：
Combining evidence from several cases / 4.5.3：
Assessment of cases / 4.6：
Similarity-weighted approximation / 4.6.1：
More general criteria / 4.6.2：
Assessment of individual cases / 4.6.3：
Complex similarity hypotheses / 4.7：
Inference schemes of higher order / 4.7.1：
Partially admissible profiles / 4.7.2：
Approximate probabilistic inference / 4.8：
Generalized uncertainty measures and profiles / 4.8.1：
An approximate inference scheme / 4.8.2：
Fuzzy Set-Based Modeling of Case-Based Inference I / 4.9：
Background on possibility theory / 5.1：
Possibility distributions as generalized constraints / 5.1.1：
Possibility as evidential support / 5.1.2：
Fuzzy rule-based modeling of the CBI hypothesis / 5.2：
Possibility rules / 5.2.1：
Modeling the CBI hypothesis / 5.2.2：
Generalized possibilistic prediction / 5.3：
Control of compensation and accumulation of support / 5.3.1：
Possibilistic support and weighted NN estimation / 5.3.2：
Upper and lower possibility bounds / 5.3.3：
Fuzzy logical evaluation / 5.3.4：
Comparison of extrapolation principles / 5.3.5：
From predictions to decisions / 5.3.6：
An illustrative example / 5.3.7：
Complexity issues / 5.3.8：
Extensions of the basic model / 5.4：
Dealing with incomplete information / 5.4.1：
Discounting noisy and atypical instances / 5.4.2：
From instances to rules / 5.4.3：
Modified possibility rules / 5.4.4：
Combination of several rules / 5.4.5：
Locally restricted extrapolation / 5.4.6：
Incorporation of background knowledge / 5.4.7：
Experimental studies / 5.5：
Preliminaries / 5.5.1：
Classification accuracy / 5.5.2：
Statistical assumptions and robustness / 5.5.3：
Variation of the aggregation operator / 5.5.4：
Representation of uncertainty / 5.5.5：
Calibration of CBI models / 5.6：
Relations to other fields / 5.7：
Fuzzy and possibilistic data analysis / 5.7.1：
Fuzzy set-based approximate reasoning / 5.7.2：
Fuzzy Set-Based Modeling of Case-Based Inference II / 5.8：
Gradual inference rules / 6.1：
The basic model / 6.1.1：
Modification of gradual rules / 6.1.2：
Certainty rules / 6.2：
Cases as information sources / 6.3：
A probabilistic model / 6.3.1：
Combination of information sources / 6.3.2：
Exceptionality and assessment of cases / 6.4：
Local rules / 6.5：
Case-Based Decision Making / 6.6：
Case-based decision theory / 7.1：
Nearest Neighbor decisions / 7.2：
Nearest Neighbor classification and decision making / 7.2.1：
Nearest Neighbor decision rules / 7.2.2：
An axiomatic characterization / 7.2.3：
Fuzzy modeling of case-based decisions / 7.3：
Basic measures for act evaluation / 7.3.1：
Modification of the basic measures / 7.3.2：
Interpretation of the decision criteria / 7.3.3：
Fuzzy quantification in act evaluation / 7.4：
A CBI framework of CBDM / 7.5：
Generalized decision-theoretic setups / 7.5.1：
Decision making using belief functions / 7.5.2：
Possibilistic decision making / 7.5.3：
CBDM models: A discussion of selected issues / 7.6：
The relation between similarity, preference, and belief / 7.6.1：
The effect of observed cases / 7.6.2：
Dynamic aspects of decision making / 7.6.3：
Experience-based decision making / 7.7：
Compiled decision models / 7.7.1：
Satisficing decision trees / 7.7.2：
Experimental evaluation / 7.7.3：
Conclusions and Outlook / 7.8：
Possibilistic Dominance in Qualitative Decisions / A：
Implication-Based Fuzzy Rules as Randomized Gradual Rules / B：
Implication-based fuzzy rules / B.1：
Gradual rules / B.1.1：
Other implication-based rules / B.1.2：
Randomized gradual rules / B.2：
A probabilistic representation of implication-based fuzzy rules / B.3：
Similarity-Based Reasoning as Logical Inference / C：
Simulation Results of Section 3.4.4 / D：
Experimental Results of Section 5.5.4 / E：
Simulation Results of Section 7.4 / F：
Computation of an Extended Splitting Measures / G：
Experimental Results of Section 7.7.2 / H：
References
Index
Dedication
Foreword
Preface
38.

電子ブック
ＥＢ
38. Fuzzy-Neuro Approach to Agent Applications
Raymond S. T. Lee, Toru Ishida, Nicholas R. Jennings
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2006
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Introduction / 1：
The Coming of the Age of Intelligent Agents / 1.1：
The Structure of This Book / 1.2：
Outline of Each Chapter / 1.3：
Readers of This Book / 1.4：
Concluding Remarks / 1.5：
Concepts and Theories / Part I：
The Search for Human Intelligence / 2：
What Is Intelligence? / 2.1：
The Philosophical View on Intelligence / 2.2：
Introduction - The Search for Intelligence and Ultimate Knowledge / 2.2.1：
The Traditional Philosophical View of Knowledge - Belief, Truth and Justification / 2.2.2：
Rationalistic Versus Empiristic View of Knowledge / 2.2.3：
Kant's Critique of Pure Reason and the Theory of Knowledge / 2.2.4：
Russell's View of Knowledge / 2.2.5：
Krishnamurti's The Awakening of Intelligence - Thought Versus Intelligence / 2.2.6：
Lee's Theory on Knowledge and Intelligence - The Unification Theory of Senses and Experiences / 2.2.7：
The Cognitive-Scientific View on Intelligence / 2.3：
The Cognitive-Scientific Definition of Intelligence / 2.3.1：
Spearman's Model of the Nature of Intelligence / 2.3.2：
Piaget's Psychology of Intelligence / 2.3.3：
Major Approaches of Intelligence - From Psychometric Approach to Latest Studies / 2.3.4：
Gardner's Theory on Multiple Intelligence / 2.3.5：
Lee's Unification Theory of Senses and Experiences - The Psychological Interpretation / 2.3.6：
The Neuroscience and Neurophysiology View on Intelligence / 2.4：
The Major Challenges of Mind Science (The Exploration of the Mind from the Neuroscience Perspective) / 2.4.1：
A Brief History - The Search for Intelligence in Neuroscience / 2.4.2：
Contemporary Research in Mind Science - From Neural Oscillators to the "Chaos in the Brain" / 2.4.3：
The Neuroscientific and Neurophysiological Implications of the Unification Theory of Senses and Experiences / 2.4.4：
Summary / 2.4.5：
From AI to IA - The Emergence of Agent Technology / 2.5：
What is AI? / 3.1：
A Brief History of AI / 3.2：
The Dartmouth Meeting (1956) - The Birth of AI / 3.2.1：
The Turing Test - A Prelude of AI / 3.2.2：
Strong Versus Weak AI / 3.2.3：
Searle's Chinese Room Thought Experiment / 3.2.4：
Development of AI in the Late 1970s / 3.2.5：
The "Reincarnation" of Neural Networks in the Late 1980s / 3.2.6：
The Birth of IAs in the Late 1990s / 3.2.7：
An Overview of the Classification of AI Technologies / 3.3：
AI - Where to Go? / 3.4：
The Coming of the Age of IAs / 3.5：
What Is an IA? - A "Right" Place to Start / 3.5.1：
The Emergence of Agent Technology - The Idea of Portable Intelligence / 3.5.2：
The Ten Basic Requirements of IAs / 3.6：
The Contemporary Variety of IAs / 3.7：
The Conceptual Model of IAs / 3.8：
The BFI Agent Intellectual Conceptual Model / 3.8.1：
The Agent Development Conceptual Model (GIA vs. TIA) / 3.8.2：
Major Challenges and Threats of Agent Technology / 3.9：
AI Techniques for Agent Construction / 3.10：
The World of Fuzziness, Chaos, and Uncertainty / 4.1：
Fuzzy Logic / 4.2：
What is Fuzzy Logic? / 4.2.1：
Fuzzy Theory and the Uncertainty Principle / 4.2.2：
Fuzzy Logic - A Structural Overview / 4.2.3：
Fuzzy Reasoning - A Case Study on Fuzzy Air-conditioning Control System / 4.2.4：
Applications of Fuzzy Logic / 4.2.5：
Neural Networks - the "Brain" of IAs / 4.3：
Neural Networks - Background / 4.3.1：
ANN Architecture / 4.3.2：
Classification of Neural Networks / 4.3.3：
Associative Memory Neural Networks: Auto-associative Networks / 4.3.4：
Hopfield Networks / 4.3.5：
Multilayer Feedforward Backpropagation Networks (FFBPNs) / 4.3.6：
Neural Networks - Where to Go? / 4.3.7：
Genetic Algorithms - the Nature of Evolution / 4.4：
Genetic Algorithms - Basic Principle / 4.4.1：
Population Initialization / 4.4.2：
Fitness Evaluation / 4.4.3：
Parent Selection Scheme / 4.4.4：
Crossover and Mutation / 4.4.5：
Implementation of GAs / 4.4.6：
Hybridization of GA with Neural Networks / 4.4.7：
Chaos Theory - The World of Nonlinear Dynamics / 4.5：
Chaos Theory - The Study of Nonlinear Dynamics / 4.5.1：
Battle Between two Worlds: Deterministic Versus Probabilistic / 4.5.2：
A Snapshot of Chaos Theory / 4.5.3：
Characteristics of Chaos Systems / 4.5.4：
Chaos Theory Versus Uncertainty Principle / 4.5.5：
Current Work on Chaos Theory / 4.5.6：
Chaotic Neural Networks and the Lee-Oscillator / 4.6：
Chaotic Neural Oscillators - An Overview / 4.6.1：
The Lee-Oscillator / 4.6.2：
The Lee-Associator / 4.6.3：
System Implementation and Experimental Results / 4.6.4：
Progressive Memory Recalling Scheme of the Lee-Associator and Its Biological and Psychological Implications / 4.6.5：
Related Work / 4.6.6：
Conclusion / 4.6.7：
Further Reading / 4.7：
Applications of Intelligent Agents Using iJADK / Part II：
The Design and Implementation of an Intelligent Agent-Based System Using iJADK / 5：
iJADE - System Framework / 5.1：
iJADE Architecture / 5.2.1：
Application Layer / 5.2.2：
Conscious (Intelligent) Layer / 5.2.3：
Technology Layer / 5.2.4：
Supporting Layer / 5.2.5：
iJADK Architecture / 5.3：
Introduction to iJADK / 5.3.1：
Basic Components of iJADK / 5.3.2：
Internal Operations of iJADK / 5.3.3：
Agent Programming Over the iJADK Platform / 5.4：
User Interface / 5.4.1：
Agent Class / 5.4.2：
LifeCycleManager / 5.4.3：
RuntimeAgent / 5.4.4：
Sample iJADE Agents / 5.5：
HelloWorldAgent / 5.5.1：
HelloWorldAgent2 / 5.5.2：
TalkAgent / 5.5.3：
Latest Works of iJADE / 5.6：
iJADE WShopper - Intelligent Mobile Shopping Based on Fuzzy-Neuro Shopping Agents / 5.7：
WAP Technology / 6.1：
WAP Technology - From Web to MEB / 6.2.1：
Constraints for Contemporary WAP Technology on MEB / 6.2.2：
iJADE WShopper - System Framework / 6.3：
iJADE WShopper - System Overview / 6.3.1：
iJADE WShopper for M-shopping - System Components / 6.3.2：
Experimental Results / 6.4：
The RTT Test / 6.4.1：
The PS Test / 6.4.3：
The iWSAS Test / 6.4.4：
Migration to the J2ME Platform / 6.5：
Incorporate Other AI Capabilities in the Shopper Agents - iJADE Negotiator / 6.6.2：
iJADE WeatherMAN - A Weather Forecasting Agent Using the Fuzzy Neural Network Model / 7：
Weather Prediction Using a Fuzzy-Neuro Model / 7.1：
iJADE WeatherMAN - System Overview / 7.3：
User Requirement Definition Scheme (URDS) and Weather Reporting Scheme (WRS) / 7.3.1：
Data Collection Scheme (DCS) / 7.3.2：
Variable Selection and Transformation Scheme (VSTS) / 7.3.3：
Fuzzy-Neuro Training and Prediction Scheme (FNTPS) / 7.3.4：
iJADE WeatherMAN - System Implementation / 7.4：
iJADE WeatherMAN Weather Site / 7.4.1：
Central Agent/Test Agent / 7.4.2：
iJADE WeatherMan Place / 7.4.3：
iJADE WeatherMan Agent / 7.4.4：
iJADE Weather Forecaster Place / 7.4.5：
iJADE Forecaster Agent / 7.4.6：
Evaluation Considerations / 7.5：
Average Classification Rate / 7.5.2：
Model Performance / 7.5.3：
The HKO Forecast / 7.5.4：
Future Work / 7.6：
iJADE Stock Advisor - An Intelligent Agent-Based Stock Prediction System Using the Hybrid RBF Recurrent Network / 8：
Stock Advisory and Prediction System - A General Overview / 8.1：
Stochastic Indicator / 8.2.1：
Relative Strength Index (RSI) / 8.2.2：
Money Flow / 8.2.3：
Moving Average / 8.2.4：
Support and Resistant Lines (Trendlines) / 8.2.5：
Trend Generalization / 8.2.6：
iJADE Stock Advisor - System Framework / 8.3：
iJADE Stock Advisor - System Overview / 8.3.1：
Stock Prediction Using the HRBF model / 8.3.2：
Parameter Selection Scheme in HRBFN / 8.4：
Round-Trip-Time (RTT) Test / 8.4.2：
Long- and Short-Term Prediction, Window Size Evaluation Test / 8.4.3：
Stock Prediction Performance Test / 8.4.4：
iJADE Surveillant - A Multi-resolution Neuro-oscillatory Agent-Based Surveillance System / 8.5：
Surveillance System - An Overview / 9.1：
Background / 9.2.1：
Scene Analysis / 9.2.2：
Human Face Recognition / 9.2.3：
Supporting Technologies / 9.3：
MPEG-7 - System Overview / 9.3.1：
MPEG-7 Model / 9.3.2：
The Latest MPEG-7 Development Work on Visual Object Modeling / 9.3.3：
iJADE Surveillant - System Overview / 9.4：
iJADE Surveillant - System Architecture / 9.4.1：
Automatic Multi-resolution Scene Segmentation Scheme Using the CNOW Model / 9.4.2：
Automatic Human Face Detection and Contour Features Extraction Using the ACM / 9.4.3：
Invariant Human Face Recognition Using the EGDLM / 9.4.4：
System Implementation / 9.5：
Automatic Color Scene Segmentation Scheme / 9.5.1：
Invariant Human Face Recognition Scheme / 9.5.2：
Facial Pattern Occlusion and Distortion Test / 9.5.3：
Performance Analysis / 9.5.4：
iJADE Negotiator - An Intelligent Fuzzy Agent-Based Negotiation System for Internet Shopping / 9.6：
Negotiation Systems - An Overview / 10.1：
iJADE Negotiator - System Architecture / 10.3：
iJADE Negotiator - System Overview / 10.3.1：
iJADE Negotiator - Main Functional Modules / 10.3.2：
iJADE Negotiator - Intelligent Negotiation Strategy and Negotiation Protocol / 10.3.3：
iJADE Negotiator - System Implementation / 10.4：
Future Agent Technology - Modern Ontology and Ontological Agent Technologies (OAT) / 10.4.1：
What Is Ontology? / 11.1：
Ontology - Theories of Existence / 11.1.1：
Universals Versus Particulars / 11.1.2：
Ontology - The World of Universals / 11.1.3：
Ontological View of the Nature of Existence / 11.1.4：
Impact of Ontology on Modern AI / 11.1.5：
Modern Ontology and Ontological Agents / 11.2：
The Theoretical Foundation of OAT - Conceptualization Theory / 11.2.1：
Characteristics of Ontological Agents / 11.2.2：
Potential Applications of OAT / 11.2.3：
Cogito iJADE Project / 11.2.4：
Cogito iJADE - A New Era of Self-aware IAs / 11.3.1：
Cogito iJADE - A System Overview / 11.3.2：
Latest Works of Cogito Agents / 11.3.3：
Agent Technology - The Future / 11.4：
iJADK 2.0 API / Appendix：
References
Index
About the Author
Introduction / 1：
The Coming of the Age of Intelligent Agents / 1.1：
The Structure of This Book / 1.2：
39.

電子ブック
ＥＢ
39. Fuzzy-Neuro Approach to Agent Applications
Raymond S. T. Lee, Toru Ishida, Nicholas R. Jennings, Katia Sycara
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2006
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1：
The Coming of the Age of Intelligent Agents / 1.1：
The Structure of This Book / 1.2：
Outline of Each Chapter / 1.3：
Readers of This Book / 1.4：
Concluding Remarks / 1.5：
Concepts and Theories / Part I：
The Search for Human Intelligence / 2：
What Is Intelligence? / 2.1：
The Philosophical View on Intelligence / 2.2：
Introduction - The Search for Intelligence and Ultimate Knowledge / 2.2.1：
The Traditional Philosophical View of Knowledge - Belief, Truth and Justification / 2.2.2：
Rationalistic Versus Empiristic View of Knowledge / 2.2.3：
Kant's Critique of Pure Reason and the Theory of Knowledge / 2.2.4：
Russell's View of Knowledge / 2.2.5：
Krishnamurti's The Awakening of Intelligence - Thought Versus Intelligence / 2.2.6：
Lee's Theory on Knowledge and Intelligence - The Unification Theory of Senses and Experiences / 2.2.7：
The Cognitive-Scientific View on Intelligence / 2.3：
The Cognitive-Scientific Definition of Intelligence / 2.3.1：
Spearman's Model of the Nature of Intelligence / 2.3.2：
Piaget's Psychology of Intelligence / 2.3.3：
Major Approaches of Intelligence - From Psychometric Approach to Latest Studies / 2.3.4：
Gardner's Theory on Multiple Intelligence / 2.3.5：
Lee's Unification Theory of Senses and Experiences - The Psychological Interpretation / 2.3.6：
The Neuroscience and Neurophysiology View on Intelligence / 2.4：
The Major Challenges of Mind Science (The Exploration of the Mind from the Neuroscience Perspective) / 2.4.1：
A Brief History - The Search for Intelligence in Neuroscience / 2.4.2：
Contemporary Research in Mind Science - From Neural Oscillators to the "Chaos in the Brain" / 2.4.3：
The Neuroscientific and Neurophysiological Implications of the Unification Theory of Senses and Experiences / 2.4.4：
Summary / 2.4.5：
From AI to IA - The Emergence of Agent Technology / 2.5：
What is AI? / 3.1：
A Brief History of AI / 3.2：
The Dartmouth Meeting (1956) - The Birth of AI / 3.2.1：
The Turing Test - A Prelude of AI / 3.2.2：
Strong Versus Weak AI / 3.2.3：
Searle's Chinese Room Thought Experiment / 3.2.4：
Development of AI in the Late 1970s / 3.2.5：
The "Reincarnation" of Neural Networks in the Late 1980s / 3.2.6：
The Birth of IAs in the Late 1990s / 3.2.7：
An Overview of the Classification of AI Technologies / 3.3：
AI - Where to Go? / 3.4：
The Coming of the Age of IAs / 3.5：
What Is an IA? - A "Right" Place to Start / 3.5.1：
The Emergence of Agent Technology - The Idea of Portable Intelligence / 3.5.2：
The Ten Basic Requirements of IAs / 3.6：
The Contemporary Variety of IAs / 3.7：
The Conceptual Model of IAs / 3.8：
The BFI Agent Intellectual Conceptual Model / 3.8.1：
The Agent Development Conceptual Model (GIA vs. TIA) / 3.8.2：
Major Challenges and Threats of Agent Technology / 3.9：
AI Techniques for Agent Construction / 3.10：
The World of Fuzziness, Chaos, and Uncertainty / 4.1：
Fuzzy Logic / 4.2：
What is Fuzzy Logic? / 4.2.1：
Fuzzy Theory and the Uncertainty Principle / 4.2.2：
Fuzzy Logic - A Structural Overview / 4.2.3：
Fuzzy Reasoning - A Case Study on Fuzzy Air-conditioning Control System / 4.2.4：
Applications of Fuzzy Logic / 4.2.5：
Neural Networks - the "Brain" of IAs / 4.3：
Neural Networks - Background / 4.3.1：
ANN Architecture / 4.3.2：
Classification of Neural Networks / 4.3.3：
Associative Memory Neural Networks: Auto-associative Networks / 4.3.4：
Hopfield Networks / 4.3.5：
Multilayer Feedforward Backpropagation Networks (FFBPNs) / 4.3.6：
Neural Networks - Where to Go? / 4.3.7：
Genetic Algorithms - the Nature of Evolution / 4.4：
Genetic Algorithms - Basic Principle / 4.4.1：
Population Initialization / 4.4.2：
Fitness Evaluation / 4.4.3：
Parent Selection Scheme / 4.4.4：
Crossover and Mutation / 4.4.5：
Implementation of GAs / 4.4.6：
Hybridization of GA with Neural Networks / 4.4.7：
Chaos Theory - The World of Nonlinear Dynamics / 4.5：
Chaos Theory - The Study of Nonlinear Dynamics / 4.5.1：
Battle Between two Worlds: Deterministic Versus Probabilistic / 4.5.2：
A Snapshot of Chaos Theory / 4.5.3：
Characteristics of Chaos Systems / 4.5.4：
Chaos Theory Versus Uncertainty Principle / 4.5.5：
Current Work on Chaos Theory / 4.5.6：
Chaotic Neural Networks and the Lee-Oscillator / 4.6：
Chaotic Neural Oscillators - An Overview / 4.6.1：
The Lee-Oscillator / 4.6.2：
The Lee-Associator / 4.6.3：
System Implementation and Experimental Results / 4.6.4：
Progressive Memory Recalling Scheme of the Lee-Associator and Its Biological and Psychological Implications / 4.6.5：
Related Work / 4.6.6：
Conclusion / 4.6.7：
Further Reading / 4.7：
Applications of Intelligent Agents Using iJADK / Part II：
The Design and Implementation of an Intelligent Agent-Based System Using iJADK / 5：
iJADE - System Framework / 5.1：
iJADE Architecture / 5.2.1：
Application Layer / 5.2.2：
Conscious (Intelligent) Layer / 5.2.3：
Technology Layer / 5.2.4：
Supporting Layer / 5.2.5：
iJADK Architecture / 5.3：
Introduction to iJADK / 5.3.1：
Basic Components of iJADK / 5.3.2：
Internal Operations of iJADK / 5.3.3：
Agent Programming Over the iJADK Platform / 5.4：
User Interface / 5.4.1：
Agent Class / 5.4.2：
LifeCycleManager / 5.4.3：
RuntimeAgent / 5.4.4：
Sample iJADE Agents / 5.5：
HelloWorldAgent / 5.5.1：
HelloWorldAgent2 / 5.5.2：
TalkAgent / 5.5.3：
Latest Works of iJADE / 5.6：
iJADE WShopper - Intelligent Mobile Shopping Based on Fuzzy-Neuro Shopping Agents / 5.7：
WAP Technology / 6.1：
WAP Technology - From Web to MEB / 6.2.1：
Constraints for Contemporary WAP Technology on MEB / 6.2.2：
iJADE WShopper - System Framework / 6.3：
iJADE WShopper - System Overview / 6.3.1：
iJADE WShopper for M-shopping - System Components / 6.3.2：
Experimental Results / 6.4：
The RTT Test / 6.4.1：
The PS Test / 6.4.3：
The iWSAS Test / 6.4.4：
Migration to the J2ME Platform / 6.5：
Incorporate Other AI Capabilities in the Shopper Agents - iJADE Negotiator / 6.6.2：
iJADE WeatherMAN - A Weather Forecasting Agent Using the Fuzzy Neural Network Model / 7：
Weather Prediction Using a Fuzzy-Neuro Model / 7.1：
iJADE WeatherMAN - System Overview / 7.3：
User Requirement Definition Scheme (URDS) and Weather Reporting Scheme (WRS) / 7.3.1：
Data Collection Scheme (DCS) / 7.3.2：
Variable Selection and Transformation Scheme (VSTS) / 7.3.3：
Fuzzy-Neuro Training and Prediction Scheme (FNTPS) / 7.3.4：
iJADE WeatherMAN - System Implementation / 7.4：
iJADE WeatherMAN Weather Site / 7.4.1：
Central Agent/Test Agent / 7.4.2：
iJADE WeatherMan Place / 7.4.3：
iJADE WeatherMan Agent / 7.4.4：
iJADE Weather Forecaster Place / 7.4.5：
iJADE Forecaster Agent / 7.4.6：
Evaluation Considerations / 7.5：
Average Classification Rate / 7.5.2：
Model Performance / 7.5.3：
The HKO Forecast / 7.5.4：
Future Work / 7.6：
iJADE Stock Advisor - An Intelligent Agent-Based Stock Prediction System Using the Hybrid RBF Recurrent Network / 8：
Stock Advisory and Prediction System - A General Overview / 8.1：
Stochastic Indicator / 8.2.1：
Relative Strength Index (RSI) / 8.2.2：
Money Flow / 8.2.3：
Moving Average / 8.2.4：
Support and Resistant Lines (Trendlines) / 8.2.5：
Trend Generalization / 8.2.6：
iJADE Stock Advisor - System Framework / 8.3：
iJADE Stock Advisor - System Overview / 8.3.1：
Stock Prediction Using the HRBF model / 8.3.2：
Parameter Selection Scheme in HRBFN / 8.4：
Round-Trip-Time (RTT) Test / 8.4.2：
Long- and Short-Term Prediction, Window Size Evaluation Test / 8.4.3：
Stock Prediction Performance Test / 8.4.4：
iJADE Surveillant - A Multi-resolution Neuro-oscillatory Agent-Based Surveillance System / 8.5：
Surveillance System - An Overview / 9.1：
Background / 9.2.1：
Scene Analysis / 9.2.2：
Human Face Recognition / 9.2.3：
Supporting Technologies / 9.3：
MPEG-7 - System Overview / 9.3.1：
MPEG-7 Model / 9.3.2：
The Latest MPEG-7 Development Work on Visual Object Modeling / 9.3.3：
iJADE Surveillant - System Overview / 9.4：
iJADE Surveillant - System Architecture / 9.4.1：
Automatic Multi-resolution Scene Segmentation Scheme Using the CNOW Model / 9.4.2：
Automatic Human Face Detection and Contour Features Extraction Using the ACM / 9.4.3：
Invariant Human Face Recognition Using the EGDLM / 9.4.4：
System Implementation / 9.5：
Automatic Color Scene Segmentation Scheme / 9.5.1：
Invariant Human Face Recognition Scheme / 9.5.2：
Facial Pattern Occlusion and Distortion Test / 9.5.3：
Performance Analysis / 9.5.4：
iJADE Negotiator - An Intelligent Fuzzy Agent-Based Negotiation System for Internet Shopping / 9.6：
Negotiation Systems - An Overview / 10.1：
iJADE Negotiator - System Architecture / 10.3：
iJADE Negotiator - System Overview / 10.3.1：
iJADE Negotiator - Main Functional Modules / 10.3.2：
iJADE Negotiator - Intelligent Negotiation Strategy and Negotiation Protocol / 10.3.3：
iJADE Negotiator - System Implementation / 10.4：
Future Agent Technology - Modern Ontology and Ontological Agent Technologies (OAT) / 10.4.1：
What Is Ontology? / 11.1：
Ontology - Theories of Existence / 11.1.1：
Universals Versus Particulars / 11.1.2：
Ontology - The World of Universals / 11.1.3：
Ontological View of the Nature of Existence / 11.1.4：
Impact of Ontology on Modern AI / 11.1.5：
Modern Ontology and Ontological Agents / 11.2：
The Theoretical Foundation of OAT - Conceptualization Theory / 11.2.1：
Characteristics of Ontological Agents / 11.2.2：
Potential Applications of OAT / 11.2.3：
Cogito iJADE Project / 11.2.4：
Cogito iJADE - A New Era of Self-aware IAs / 11.3.1：
Cogito iJADE - A System Overview / 11.3.2：
Latest Works of Cogito Agents / 11.3.3：
Agent Technology - The Future / 11.4：
iJADK 2.0 API / Appendix：
References
Index
About the Author
Introduction / 1：
The Coming of the Age of Intelligent Agents / 1.1：
The Structure of This Book / 1.2：
40.

電子ブック
ＥＢ
40. Evolutionary Optimization: the ?GP toolkit
Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
出版情報: Springer eBooks Computer Science , Springer US, 2011
所蔵情報: loading…
目次情報: 続きを見る
Evolutionary computation / 1：
Natural and artificial evolution / 1.1：
The classical paradigms / 1.2：
Genetic programming / 1.3：
Why yet another one evolutionary optimizer? / 2：
Background / 2.1：
Where to draw the lines / 2.2：
Individuals / 2.3：
Problem specification / 2.4：
Coding Techniques / 2.5：
The ?Gp architecture / 3：
Conceptual design / 3.1：
The evolutionary core / 3.2：
Evolutionary Operators / 3.2.1：
Population / 3.2.2：
The Evolutionary Cycle / 3.3：
Genetic operator selection / 3.3.1：
Parents selection / 3.3.2：
Offspring Generation / 3.3.3：
Individual Evaluation and Slaughtering / 3.3.4：
Termination and Aging / 3.3.5：
Advanced features / 4：
Self adaptation for exploration or exploitation / 4.1：
Self-adaptation inertia / 4.1.1：
Operator strength / 4.1.2：
Tournament size / 4.3.3：
Escaping local optimums / 4.2：
Operator activation probability / 4.2.1：
Tuning the elitism / 4.2.2：
Preserving diversity / 4.3：
Clone detection, scaling and extermination / 4.3.1：
Entropy and delta-entropy computation / 4.3.2：
Fitness holes
Population topology and multiple populations
Coping with the real problems / 4.4：
Parallel fitness evaluation / 4.4.1：
Multiple fitness / 4.4.2：
Performing an evolutionary run / 5：
Robot Pathfinder / 5.1：
?Gp Settings / 5.2：
Population Settings / 5.3：
Library of Constraints / 5.4：
Launching the experiment / 5.5：
?Gp Extractor / 5.6：
Command line syntax / 6：
Starting a run / 6.1：
Controlling messages to the user / 6.2：
Getting help and information / 6.3：
Controlling logging / 6.4：
Controlling recovery / 6.5：
Controlling evolution / 6.6：
Controlling evaluation / 6.7：
Syntax of the settings file / 7：
Syntax of the population parameters file / 7.1：
Strategy parameters / 8.1：
Base parameters / 8.1.1：
Parameters for self adaptation / 8.1.2：
Other parameters / 8.1.3：
Syntax of the external constraints file / 9：
Purposes of the constraints / 9.1：
Organization of constraints and hierarchy / 9.2：
Specifying the structure of the individual / 9.3：
Specifying the contents of the individual / 9.4：
Writing a compliant evaluator / 10：
Information from ?Gp to the fitness evaluator / 10.1：
Expected fitness format / 10.2：
Good Examples / 10.2.1：
Bad Examples / 10.2.2：
Implementation details / 11：
Design principles / 11.1：
Architectural choices / 11.2：
The Graph library / 11.2.1：
The Evolutionary Core library / 11.2.2：
Front end / 11.2.3：
Code organization and class model / 11.3：
Examples and applications / 12：
Classical one-max / 12.1：
Fitness evaluator / 12.1.1：
Constraints / 12.1.2：
Population settings / 12.1.3：
?Gp settings / 12.1.4：
Running / 12.1.5：
Values of parameters and their influence on the evolution: Arithmetic expressions / 13412.2：
De Jong 3 / 12.2.1：
De Jong 4-Modified / 12.2.2：
Carrom / 12.2.3：
Complex individuals' structures and evaluation: Bit-counting in Assembly / 12.3：
Assembly individuals representation / 12.3.1：
Evaluator / 12.3.2：
Argument and option synopsis / 12.3.3：
External constraints synopsis
References
Evolutionary computation / 1：
Natural and artificial evolution / 1.1：
The classical paradigms / 1.2：
41.

電子ブック
ＥＢ
41. Evolutionary Optimization: the µGP toolkit
Ernesto Sanchez, Massimiliano Schillaci, Giovanni Squillero
出版情報: SpringerLink Books - AutoHoldings , Springer US, 2011
所蔵情報: loading…
目次情報: 続きを見る
Evolutionary computation / 1：
Natural and artificial evolution / 1.1：
The classical paradigms / 1.2：
Genetic programming / 1.3：
Why yet another one evolutionary optimizer? / 2：
Background / 2.1：
Where to draw the lines / 2.2：
Individuals / 2.3：
Problem specification / 2.4：
Coding Techniques / 2.5：
The ?Gp architecture / 3：
Conceptual design / 3.1：
The evolutionary core / 3.2：
Evolutionary Operators / 3.2.1：
Population / 3.2.2：
The Evolutionary Cycle / 3.3：
Genetic operator selection / 3.3.1：
Parents selection / 3.3.2：
Offspring Generation / 3.3.3：
Individual Evaluation and Slaughtering / 3.3.4：
Termination and Aging / 3.3.5：
Advanced features / 4：
Self adaptation for exploration or exploitation / 4.1：
Self-adaptation inertia / 4.1.1：
Operator strength / 4.1.2：
Tournament size / 4.3.3：
Escaping local optimums / 4.2：
Operator activation probability / 4.2.1：
Tuning the elitism / 4.2.2：
Preserving diversity / 4.3：
Clone detection, scaling and extermination / 4.3.1：
Entropy and delta-entropy computation / 4.3.2：
Fitness holes
Population topology and multiple populations
Coping with the real problems / 4.4：
Parallel fitness evaluation / 4.4.1：
Multiple fitness / 4.4.2：
Performing an evolutionary run / 5：
Robot Pathfinder / 5.1：
?Gp Settings / 5.2：
Population Settings / 5.3：
Library of Constraints / 5.4：
Launching the experiment / 5.5：
?Gp Extractor / 5.6：
Command line syntax / 6：
Starting a run / 6.1：
Controlling messages to the user / 6.2：
Getting help and information / 6.3：
Controlling logging / 6.4：
Controlling recovery / 6.5：
Controlling evolution / 6.6：
Controlling evaluation / 6.7：
Syntax of the settings file / 7：
Syntax of the population parameters file / 7.1：
Strategy parameters / 8.1：
Base parameters / 8.1.1：
Parameters for self adaptation / 8.1.2：
Other parameters / 8.1.3：
Syntax of the external constraints file / 9：
Purposes of the constraints / 9.1：
Organization of constraints and hierarchy / 9.2：
Specifying the structure of the individual / 9.3：
Specifying the contents of the individual / 9.4：
Writing a compliant evaluator / 10：
Information from ?Gp to the fitness evaluator / 10.1：
Expected fitness format / 10.2：
Good Examples / 10.2.1：
Bad Examples / 10.2.2：
Implementation details / 11：
Design principles / 11.1：
Architectural choices / 11.2：
The Graph library / 11.2.1：
The Evolutionary Core library / 11.2.2：
Front end / 11.2.3：
Code organization and class model / 11.3：
Examples and applications / 12：
Classical one-max / 12.1：
Fitness evaluator / 12.1.1：
Constraints / 12.1.2：
Population settings / 12.1.3：
?Gp settings / 12.1.4：
Running / 12.1.5：
Values of parameters and their influence on the evolution: Arithmetic expressions / 13412.2：
De Jong 3 / 12.2.1：
De Jong 4-Modified / 12.2.2：
Carrom / 12.2.3：
Complex individuals' structures and evaluation: Bit-counting in Assembly / 12.3：
Assembly individuals representation / 12.3.1：
Evaluator / 12.3.2：
Argument and option synopsis / 12.3.3：
External constraints synopsis
References
Evolutionary computation / 1：
Natural and artificial evolution / 1.1：
The classical paradigms / 1.2：
42.

電子ブック
ＥＢ
42. Guide to Intelligent Data Analysis
Michael R. Berthold, Christian Borgelt, Frank H?ppner, Frank Klawonn
出版情報: Springer eBooks Computer Science , Springer London, 2010
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1：
Motivation / 1.1：
Data and Knowledge / 1.1.1：
Tycho Brahe and Johannes Kepler / 1.1.2：
Intelligent Data Analysis / 1.1.3：
The Data Analysis Process / 1.2：
Methods, Tasks, and Tools / 1.3：
How to Read This Book / 1.4：
References
Practical Data Analysis: An Example / 2：
The Setup / 2.1：
Data Understanding and Pattern Finding / 2.2：
Explanation Finding / 2.3：
Predicting the Future / 2.4：
Concluding Remarks / 2.5：
Project Understanding / 3：
Determine the Project Objective / 3.1：
Assess the Situation / 3.2：
Determine Analysis Goals / 3.3：
Further Reading / 3.4：
Data Understanding / 4：
Attribute Understanding / 4.1：
Data Quality / 4.2：
Data Visualization / 4.3：
Methods for One and Two Attributes / 4.3.1：
Methods for Higher-Dimensional Data / 4.3.2：
Correlation Analysis / 4.4：
Outlier Detection / 4.5：
Outlier Detection for Single Attributes / 4.5.1：
Outlier Detection for Multidimensional Data / 4.5.2：
Missing Values / 4.6：
A Checklist for Data Understanding / 4.7：
Data Understanding in Practice / 4.8：
Data Understanding in KNIME / 4.8.1：
Data Understanding in R / 4.8.2：
Principles of Modeling / 5：
Model Classes / 5.1：
Fitting Criteria and Score Functions / 5.2：
Error Functions for Classification Problems / 5.2.1：
Measures of Interestingness / 5.2.2：
Algorithms for Model Fitting / 5.3：
Closed Form Solutions / 5.3.1：
Gradient Method / 5.3.2：
Combinatorial Optimization / 5.3.3：
Random Search, Greedy Strategies, and Other Heuristics / 5.3.4：
Types of Errors / 5.4：
Experimental Error / 5.4.1：
Sample Error / 5.4.2：
Model Error / 5.4.3：
Algorithmic Error / 5.4.4：
Machine Learning Bias and Variance / 5.4.5：
Learning Without Bias? / 5.4.6：
Model Validation / 5.5：
Training and Test Data / 5.5.1：
Cross-Validation / 5.5.2：
Bootstrapping / 5.5.3：
Measures for Model Complexity / 5.5.4：
Model Errors and Validation in Practice / 5.6：
Errors and Validation in KNIME / 5.6.1：
Validation in R / 5.6.2：
Data Preparation / 5.7：
Select Data / 6.1：
Feature Selection / 6.1.1：
Dimensionality Reduction / 6.1.2：
Record Selection / 6.1.3：
Clean Data / 6.2：
Improve Data Quality / 6.2.1：
Construct Data / 6.2.2：
Provide Operability / 6.3.1：
Assure Impartially / 6.3.2：
Maximize Efficiency / 6.3.3：
Complex Data Types / 6.4：
Data Integration / 6.5：
Vertical Data Integration / 6.5.1：
Horizontal Data Integration / 6.5.2：
Data Preparation in Practice / 6.6：
Data Preparation in KNIME / 6.6.1：
Data Preparation in R / 6.6.2：
Finding Patterns / 7：
Hierarchical Clustering / 7.1：
Overview / 7.1.1：
Construction / 7.1.2：
Variations and Issues / 7.1.3：
Notion of (Dis-)Similarity / 7.2：
Prototype-and Model-Based Clustering / 7.3：
Density-Based Clustering / 7.3.1：
Self-organizing Maps / 7.4.1：
Frequent Pattern Mining and Association Rules / 7.5.1：
Deviation Analysis / 7.6.1：
Finding Patterns in Practice / 7.7.1：
Finding Patterns with KNIME / 7.8.1：
Finding Patterns in R / 7.8.2：
Finding Explanations / 7.9：
Decision Trees / 8.1：
Bayes Classifiers / 8.1.1：
Regression / 8.2.1：
Two Class Problems / 8.3.1：
Rule learning / 8.4：
Prepositional Rules / 8.4.1：
Inductive Logic Programming or First-Order Rules / 8.4.2：
Finding Explanations in Practice / 8.5：
Finding Explanations with KNIME / 8.5.1：
Using Explanations with R / 8.5.2：
Finding Predictors / 8.6：
Nearest-Neighbor Predictors / 9.1：
Artifical Neural Networks / 9.1.1：
Support Vector Machines / 9.2.1：
Ensemble Methods / 9.3.1：
Finding Predictors in Practice / 9.4.1：
Finding Predictors with KNIME / 9.5.1：
Using Predictors in R / 9.5.2：
Evaluation and Deployment / 10：
Evaluation / 10.1：
Deployment and Monitoring / 10.2：
Statistics / A：
Terms and Notation / A.1：
Descriptive Statistics / A.2：
Tabular Representations / A.2.1：
Graphical Representations / A.2.2：
Characteristic Measures for One-Dimensional Data / A.2.3：
Characteristic Measures for Multidimensional Data / A.2.4：
Principal Component Analysis / A.2.5：
Probability Theory / A.3：
Probability / A.3.1：
Basic Methods and Theorems / A.3.2：
Random Variables / A.3.3：
Characteristic Measures of Random Variables / A.3.4：
Some Special Distributions / A.3.5：
Inferential Statistics / A.4：
Random Samples / A.4.1：
Parameter Estimation / A.4.2：
Hypothesis Testing / A.4.3：
The R Project / B：
Installation and Overview / B.1：
Reading Files and R Objects / B.2：
R Functions and Commands / B.3：
Libraries/Packages / B.4：
R Workspace / B.5：
Finding Help / B.6：
Knime / B.7：
Building Workflows / C.1：
Example Flow / C.3：
R Integration / C.4：
Index / Appendix A：
Introduction / 1：
Motivation / 1.1：
Data and Knowledge / 1.1.1：
43.

電子ブック
ＥＢ
43. Guide to Intelligent Data Analysis
Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
出版情報: SpringerLink Books - AutoHoldings , Springer London, 2010
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1：
Motivation / 1.1：
Data and Knowledge / 1.1.1：
Tycho Brahe and Johannes Kepler / 1.1.2：
Intelligent Data Analysis / 1.1.3：
The Data Analysis Process / 1.2：
Methods, Tasks, and Tools / 1.3：
How to Read This Book / 1.4：
References
Practical Data Analysis: An Example / 2：
The Setup / 2.1：
Data Understanding and Pattern Finding / 2.2：
Explanation Finding / 2.3：
Predicting the Future / 2.4：
Concluding Remarks / 2.5：
Project Understanding / 3：
Determine the Project Objective / 3.1：
Assess the Situation / 3.2：
Determine Analysis Goals / 3.3：
Further Reading / 3.4：
Data Understanding / 4：
Attribute Understanding / 4.1：
Data Quality / 4.2：
Data Visualization / 4.3：
Methods for One and Two Attributes / 4.3.1：
Methods for Higher-Dimensional Data / 4.3.2：
Correlation Analysis / 4.4：
Outlier Detection / 4.5：
Outlier Detection for Single Attributes / 4.5.1：
Outlier Detection for Multidimensional Data / 4.5.2：
Missing Values / 4.6：
A Checklist for Data Understanding / 4.7：
Data Understanding in Practice / 4.8：
Data Understanding in KNIME / 4.8.1：
Data Understanding in R / 4.8.2：
Principles of Modeling / 5：
Model Classes / 5.1：
Fitting Criteria and Score Functions / 5.2：
Error Functions for Classification Problems / 5.2.1：
Measures of Interestingness / 5.2.2：
Algorithms for Model Fitting / 5.3：
Closed Form Solutions / 5.3.1：
Gradient Method / 5.3.2：
Combinatorial Optimization / 5.3.3：
Random Search, Greedy Strategies, and Other Heuristics / 5.3.4：
Types of Errors / 5.4：
Experimental Error / 5.4.1：
Sample Error / 5.4.2：
Model Error / 5.4.3：
Algorithmic Error / 5.4.4：
Machine Learning Bias and Variance / 5.4.5：
Learning Without Bias? / 5.4.6：
Model Validation / 5.5：
Training and Test Data / 5.5.1：
Cross-Validation / 5.5.2：
Bootstrapping / 5.5.3：
Measures for Model Complexity / 5.5.4：
Model Errors and Validation in Practice / 5.6：
Errors and Validation in KNIME / 5.6.1：
Validation in R / 5.6.2：
Data Preparation / 5.7：
Select Data / 6.1：
Feature Selection / 6.1.1：
Dimensionality Reduction / 6.1.2：
Record Selection / 6.1.3：
Clean Data / 6.2：
Improve Data Quality / 6.2.1：
Construct Data / 6.2.2：
Provide Operability / 6.3.1：
Assure Impartially / 6.3.2：
Maximize Efficiency / 6.3.3：
Complex Data Types / 6.4：
Data Integration / 6.5：
Vertical Data Integration / 6.5.1：
Horizontal Data Integration / 6.5.2：
Data Preparation in Practice / 6.6：
Data Preparation in KNIME / 6.6.1：
Data Preparation in R / 6.6.2：
Finding Patterns / 7：
Hierarchical Clustering / 7.1：
Overview / 7.1.1：
Construction / 7.1.2：
Variations and Issues / 7.1.3：
Notion of (Dis-)Similarity / 7.2：
Prototype-and Model-Based Clustering / 7.3：
Density-Based Clustering / 7.3.1：
Self-organizing Maps / 7.4.1：
Frequent Pattern Mining and Association Rules / 7.5.1：
Deviation Analysis / 7.6.1：
Finding Patterns in Practice / 7.7.1：
Finding Patterns with KNIME / 7.8.1：
Finding Patterns in R / 7.8.2：
Finding Explanations / 7.9：
Decision Trees / 8.1：
Bayes Classifiers / 8.1.1：
Regression / 8.2.1：
Two Class Problems / 8.3.1：
Rule learning / 8.4：
Prepositional Rules / 8.4.1：
Inductive Logic Programming or First-Order Rules / 8.4.2：
Finding Explanations in Practice / 8.5：
Finding Explanations with KNIME / 8.5.1：
Using Explanations with R / 8.5.2：
Finding Predictors / 8.6：
Nearest-Neighbor Predictors / 9.1：
Artifical Neural Networks / 9.1.1：
Support Vector Machines / 9.2.1：
Ensemble Methods / 9.3.1：
Finding Predictors in Practice / 9.4.1：
Finding Predictors with KNIME / 9.5.1：
Using Predictors in R / 9.5.2：
Evaluation and Deployment / 10：
Evaluation / 10.1：
Deployment and Monitoring / 10.2：
Statistics / A：
Terms and Notation / A.1：
Descriptive Statistics / A.2：
Tabular Representations / A.2.1：
Graphical Representations / A.2.2：
Characteristic Measures for One-Dimensional Data / A.2.3：
Characteristic Measures for Multidimensional Data / A.2.4：
Principal Component Analysis / A.2.5：
Probability Theory / A.3：
Probability / A.3.1：
Basic Methods and Theorems / A.3.2：
Random Variables / A.3.3：
Characteristic Measures of Random Variables / A.3.4：
Some Special Distributions / A.3.5：
Inferential Statistics / A.4：
Random Samples / A.4.1：
Parameter Estimation / A.4.2：
Hypothesis Testing / A.4.3：
The R Project / B：
Installation and Overview / B.1：
Reading Files and R Objects / B.2：
R Functions and Commands / B.3：
Libraries/Packages / B.4：
R Workspace / B.5：
Finding Help / B.6：
Knime / B.7：
Building Workflows / C.1：
Example Flow / C.3：
R Integration / C.4：
Index / Appendix A：
Introduction / 1：
Motivation / 1.1：
Data and Knowledge / 1.1.1：
44.

電子ブック
ＥＢ
44. Adaptive Business Intelligence
Zbigniew Michalewicz, Martin Schmidt
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2006
所蔵情報: loading…
目次情報: 続きを見る
Complex Business Problems / Part I：
Introduction / 1：
Characteristics of Complex Business Problems / 2：
Number of Possible Solutions / 2.1：
Time-Changing Environment / 2.2：
Problem-Specific Constraints / 2.3：
Multi-objective Problems / 2.4：
Modeling the Problem / 2.5：
A Real-World Example / 2.6：
An Extended Example: Car Distribution / 3：
Basic Terminology / 3.1：
Off-lease Cars / 3.2：
The Problem / 3.3：
Transportation / 3.4：
Volume Effect / 3.5：
Price Depreciation and Inventory / 3.6：
Dynamic Market Changes / 3.7：
The Solution / 3.8：
Adaptive Business Intelligence / 4：
Data Mining / 4.1：
Prediction / 4.2：
Optimization / 4.3：
Adaptability / 4.4：
The Structure of an Adaptive Business Intelligence System / 4.5：
Prediction and Optimization / Part II：
Prediction Methods and Models / 5：
Data Preparation / 5.1：
Different Prediction Methods / 5.2：
Mathematical Methods / 5.2.1：
Distance Methods / 5.2.2：
Logic Methods / 5.2.3：
Modern Heuristic Methods / 5.2.4：
Additional Considerations / 5.2.5：
Evaluation of Models / 5.3：
Recommended Reading / 5.4：
Modern Optimization Techniques / 6：
Overview / 6.1：
Local Optimization Techniques / 6.2：
Stochastic Hill Climber / 6.3：
Simulated Annealing / 6.4：
Tabu Search / 6.5：
Evolutionary Algorithms / 6.6：
Constraint Handling / 6.7：
Additional Issues / 6.8：
Fuzzy Logic / 6.9：
Fuzzifier / 7.1：
Inference System / 7.3：
Defuzzifier / 7.4：
Tuning the Membership Functions and Rule Base / 7.5：
Artificial Neural Networks / 7.6：
Node Input and Output / 8.1：
Different Types of Networks / 8.3：
Feed-Forward Neural Networks / 8.3.1：
Recurrent Neural Networks / 8.3.2：
Learning Methods / 8.4：
Supervised Learning / 8.4.1：
Unsupervised Learning / 8.4.2：
Data Representation / 8.5：
Other Methods and Techniques / 8.6：
Genetic Programming / 9.1：
Ant Systems and Swarm Intelligence / 9.2：
Agent-Based Modeling / 9.3：
Co-evolution / 9.4：
Hybrid Systems and Adaptability / 9.5：
Hybrid Systems for Prediction / 10.1：
Hybrid Systems for Optimization / 10.2：
Car Distribution System / 10.3：
Graphical User Interface / 11.1：
Reporting / 11.2.1：
Prediction Module / 11.3：
Optimization Module / 11.4：
Adaptability Module / 11.5：
Validation / 11.6：
Applying Adaptive Business Intelligence / 12：
Marketing Campaigns / 12.1：
Manufacturing / 12.2：
Investment Strategies / 12.3：
Emergency Response Services / 12.4：
Credit Card Fraud / 12.5：
Conclusion / 13：
Index
Complex Business Problems / Part I：
Introduction / 1：
Characteristics of Complex Business Problems / 2：
45.

電子ブック
ＥＢ
45. Adaptive Business Intelligence
Zbigniew Michalewicz, Martin Schmidt, Constantin Chiriac, Matthew Michalewicz
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2006
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Complex Business Problems / Part I：
Introduction / 1：
Characteristics of Complex Business Problems / 2：
Number of Possible Solutions / 2.1：
Time-Changing Environment / 2.2：
Problem-Specific Constraints / 2.3：
Multi-objective Problems / 2.4：
Modeling the Problem / 2.5：
A Real-World Example / 2.6：
An Extended Example: Car Distribution / 3：
Basic Terminology / 3.1：
Off-lease Cars / 3.2：
The Problem / 3.3：
Transportation / 3.4：
Volume Effect / 3.5：
Price Depreciation and Inventory / 3.6：
Dynamic Market Changes / 3.7：
The Solution / 3.8：
Adaptive Business Intelligence / 4：
Data Mining / 4.1：
Prediction / 4.2：
Optimization / 4.3：
Adaptability / 4.4：
The Structure of an Adaptive Business Intelligence System / 4.5：
Prediction and Optimization / Part II：
Prediction Methods and Models / 5：
Data Preparation / 5.1：
Different Prediction Methods / 5.2：
Mathematical Methods / 5.2.1：
Distance Methods / 5.2.2：
Logic Methods / 5.2.3：
Modern Heuristic Methods / 5.2.4：
Additional Considerations / 5.2.5：
Evaluation of Models / 5.3：
Recommended Reading / 5.4：
Modern Optimization Techniques / 6：
Overview / 6.1：
Local Optimization Techniques / 6.2：
Stochastic Hill Climber / 6.3：
Simulated Annealing / 6.4：
Tabu Search / 6.5：
Evolutionary Algorithms / 6.6：
Constraint Handling / 6.7：
Additional Issues / 6.8：
Fuzzy Logic / 6.9：
Fuzzifier / 7.1：
Inference System / 7.3：
Defuzzifier / 7.4：
Tuning the Membership Functions and Rule Base / 7.5：
Artificial Neural Networks / 7.6：
Node Input and Output / 8.1：
Different Types of Networks / 8.3：
Feed-Forward Neural Networks / 8.3.1：
Recurrent Neural Networks / 8.3.2：
Learning Methods / 8.4：
Supervised Learning / 8.4.1：
Unsupervised Learning / 8.4.2：
Data Representation / 8.5：
Other Methods and Techniques / 8.6：
Genetic Programming / 9.1：
Ant Systems and Swarm Intelligence / 9.2：
Agent-Based Modeling / 9.3：
Co-evolution / 9.4：
Hybrid Systems and Adaptability / 9.5：
Hybrid Systems for Prediction / 10.1：
Hybrid Systems for Optimization / 10.2：
Car Distribution System / 10.3：
Graphical User Interface / 11.1：
Reporting / 11.2.1：
Prediction Module / 11.3：
Optimization Module / 11.4：
Adaptability Module / 11.5：
Validation / 11.6：
Applying Adaptive Business Intelligence / 12：
Marketing Campaigns / 12.1：
Manufacturing / 12.2：
Investment Strategies / 12.3：
Emergency Response Services / 12.4：
Credit Card Fraud / 12.5：
Conclusion / 13：
Index
Complex Business Problems / Part I：
Introduction / 1：
Characteristics of Complex Business Problems / 2：
46.

電子ブック
ＥＢ
46. Logical and Relational Learning
Luc De Raedt, J?rg Siekmann
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2008
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Introduction / 1：
What Is Logical and Relational Learning? / 1.1：
Why Is Logical and Relational Learning Important? / 1.2：
Structure Activity Relationship Prediction / 1.2.1：
A Web Mining Example / 1.2.2：
A Language Learning Example / 1.2.3：
How Does Relational and Logical Learning Work? / 1.3：
A Brief History / 1.4：
An Introduction to Logic / 2：
A Relational Database Example / 2.1：
The Syntax of Clausal Logic / 2.2：
The Semantics of Clausal Logic - Model Theory / 2.3：
Inference with Clausal Logic - Proof Theory / 2.4：
Prolog and SLD-resolution / 2.5：
Historical and Bibliographic Remarks / 2.6：
An Introduction to Learning and Search / 3：
Representing Hypotheses and Instances / 3.1：
Boolean Data / 3.2：
Machine Learning / 3.3：
Data Mining / 3.4：
A Generate-and-Test Algorithm / 3.5：
Structuring the Search Space / 3.6：
Monotonicity / 3.7：
Borders / 3.8：
Refinement Operators / 3.9：
A Generic Algorithm for Mining and Learning / 3.10：
A Complete General-to-Specific Algorithm / 3.11：
A Heuristic General-to-Specific Algorithm / 3.12：
A Branch-and-Bound Algorithm / 3.13：
A Specific-to-General Algorithm / 3.14：
Working with Borders* / 3.15：
Computing a Single Border / 3.15.1：
Computing Two Borders / 3.15.2：
Computing Two Borders Incrementally / 3.15.3：
Operations on Borders / 3.15.4：
Conclusions / 3.16：
Bibliographical Notes / 3.17：
Representations for Mining and Learning / 4：
Representing Data and Hypotheses / 4.1：
Attribute-Value Learning / 4.2：
Multiple-Instance Learning: Dealing With Sets / 4.3：
Relational Learning / 4.4：
Logic Programs / 4.5：
Sequences, Lists, and Grammars / 4.6：
Trees and Terms / 4.7：
Graphs / 4.8：
Background Knowledge / 4.9：
Designing It Yourself / 4.10：
A Hierarchy of Representations* / 4.11：
From AV to BL / 4.11.1：
From MI to AV / 4.11.2：
From RL to MI / 4.11.3：
From LP to RL / 4.11.4：
Propositionalization / 4.12：
A Table-Based Approach / 4.12.1：
A Query-Based Approach / 4.12.2：
Aggregation / 4.13：
Historical and Bibliographical Remarks / 4.14：
Generality and Logical Entailment / 5：
Generality and Logical Entailment Coincide / 5.1：
Propositional Subsumption / 5.2：
Subsumption in Logical Atoms / 5.3：
Specialization Operators / 5.3.1：
Generalization Operators* / 5.3.2：
Computing the lgg and the glb / 5.3.3：
[Theta]-Subsumption / 5.4：
Soundness and Completeness / 5.4.1：
Deciding [Theta]-Subsumption / 5.4.2：
Equivalence Classes / 5.4.3：
Variants of [Theta]-Subsumption* / 5.5：
Object Identity* / 5.5.1：
Inverse Implication* / 5.5.2：
Using Background Knowledge / 5.6：
Saturation and Bottom Clauses / 5.6.1：
Relative Least General Generalization* / 5.6.2：
Semantic Refinement* / 5.6.3：
Aggregation* / 5.7：
Inverse Resolution / 5.8：
A Note on Graphs, Trees, and Sequences / 5.9：
Bibliographic Notes / 5.10：
The Upgrading Story / 6：
Motivation for a Methodology / 6.1：
Methodological Issues / 6.2：
Representing the Examples / 6.2.1：
Representing the Hypotheses / 6.2.2：
Adapting the Algorithm / 6.2.3：
Adding Features / 6.2.4：
Case Study 1: Rule Learning and Foil / 6.3：
Foil's Problem Setting / 6.3.1：
Foil's Algorithm / 6.3.2：
Case Study 2: Decision Tree Learning and Tilde / 6.4：
The Problem Setting / 6.4.1：
Inducing Logical Decision Trees / 6.4.2：
Case Study 3: Frequent Item-Set Mining and Warmr / 6.5：
Relational Association Rules and Local Patterns / 6.5.1：
Computing Frequent Queries / 6.5.2：
Language Bias / 6.6：
Syntactic Bias / 6.6.1：
Semantic Bias / 6.6.2：
Inducing Theories / 6.7：
Introduction to Theory Revision / 7.1：
Theories and Model Inference / 7.1.1：
Theory Revision / 7.1.2：
Overview of the Rest of This Chapter / 7.1.3：
Towards Abductive Logic Programming / 7.2：
Abduction / 7.2.1：
Integrity Constraints / 7.2.2：
Abductive Logic Programming / 7.2.3：
Shapiro's Theory Revision System / 7.3：
Interaction / 7.3.1：
The Model Inference System / 7.3.2：
Two Propositional Theory Revision Systems* / 7.4：
Learning a Propositional Horn Theory Efficiently / 7.4.1：
Heuristic Search in Theory Revision / 7.4.2：
Inducing Constraints / 7.5：
Problem Specification / 7.5.1：
An Algorithm for Inducing Integrity Constraints / 7.5.2：
Probabilistic Logic Learning / 7.6：
Probability Theory Review / 8.1：
Probabilistic Logics / 8.2：
Probabilities on Interpretations / 8.2.1：
Probabilities on Proofs / 8.2.2：
Probabilistic Learning / 8.3：
Parameter Estimation / 8.3.1：
Structure Learning / 8.3.2：
First-Order Probabilistic Logics / 8.4：
Probabilistic Interpretations / 8.4.1：
Probabilistic Proofs / 8.4.2：
Learning from Interpretations / 8.5：
Learning from Entailment / 8.5.2：
Learning from Proof Trees and Traces / 8.5.3：
Relational Reinforcement Learning* / 8.6：
Markov Decision Processes / 8.6.1：
Solving Markov Decision Processes / 8.6.2：
Relational Markov Decision Processes / 8.6.3：
Solving Relational Markov Decision Processes / 8.6.4：
Kernels and Distances for Structured Data / 8.7：
A Simple Kernel and Distance / 9.1：
Kernel Methods / 9.2：
The Max Margin Approach / 9.2.1：
Support Vector Machines / 9.2.2：
The Kernel Trick / 9.2.3：
Distance-Based Learning / 9.3：
Distance Functions / 9.3.1：
The k-Nearest Neighbor Algorithm / 9.3.2：
The k-Means Algorithm / 9.3.3：
Kernels for Structured Data / 9.4：
Convolution and Decomposition / 9.4.1：
Vectors and Tuples / 9.4.2：
Sets and Multi-sets / 9.4.3：
Strings / 9.4.4：
Trees and Atoms / 9.4.5：
Graph Kernels* / 9.4.6：
Distances and Metrics / 9.5：
Generalization and Metrics / 9.5.1：
Sets / 9.5.2：
Atoms and Trees / 9.5.4：
Relational Kernels and Distances / 9.5.6：
Bibliographical and Historical Notes / 9.7：
Computational Aspects of Logical and Relational Learning / 10：
Efficiency of Relational Learning / 10.1：
Coverage as [theta]-Subsumption / 10.1.1：
[theta]-Subsumption Empirically / 10.1.2：
Optimizing the Learner for [theta]-subsumption / 10.1.3：
Computational Learning Theory* / 10.2：
Notions of Learnability / 10.2.1：
Positive Results / 10.2.2：
Negative Results / 10.2.3：
Historical and Bibliographic Notes / 10.3：
Lessons Learned / 11：
A Hierarchy of Representations / 11.1：
From Upgrading to Downgrading / 11.2：
Propositionalization and Aggregation / 11.3：
Learning Tasks / 11.4：
Operators and Generality / 11.5：
Unification and Variables / 11.6：
Three Learning Settings / 11.7：
Knowledge and Background Knowledge / 11.8：
Applications / 11.9：
References
Author Index
Index
Introduction / 1：
What Is Logical and Relational Learning? / 1.1：
Why Is Logical and Relational Learning Important? / 1.2：
47.

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ＥＢ
47. Intelligent Environments
Yoshinori Kuno., Yoshinori Kuno, Dorothy Monekosso, Paolo Remagnino
出版情報: Springer eBooks Computer Science , Springer London, 2009
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Preface
List of Contributors
Intelligent Environments: Methods, Algorithms and Applications / Dorothy N. Monekosso ; Paolo Remagnino ; Yoshinori Kuno1：
Intelligent Environments / 1.1：
What Is An Intelligent Environment? / 1.1.1：
How Is An Intelligent Environment Built? / 1.1.2：
Technology for Intelligent Environments / 1.2：
Research Projects / 1.3：
Private Spaces / 1.3.1：
Public Spaces / 1.3.2：
Middleware / 1.3.3：
Chapter Themes in This Collection / 1.4：
Conclusion / 1.5：
References
A Pervasive Sensor System for Evidence-Based Nursing Care Support / Toshio Hori ; Yoshifumi Nishida ; Shin'ichi Murakami2：
Introduction / 2.1：
Evidence-Based Nursing Care Support / 2.2：
Background of the Project / 2.2.1：
Concept of Evidence-Based Nursing Care Support / 2.2.2：
Initial Goal of the Project: Falls Prevention / 2.2.3：
Second Goal of the Project: Obtaining ADL of Inhabitants / 2.2.4：
Related Work / 2.3：
Overview and Implementations of the System / 2.4：
Overview of the Evidence-Based Nursing Care Support System / 2.4.1：
System Implementations / 2.4.2：
Experiments and Analyses / 2.5：
Tracking a Wheelchair for Falls Prevention / 2.5.1：
Activity Transition Diagram: Transition of Activities in One Day / 2.5.2：
Quantitative Evaluation of Daily Activities / 2.5.3：
Probability of "Toilet" Activity / 2.5.4：
Discussion of the Experimental Results / 2.5.5：
Prospect of the Evidence-Based Nursing Care Support System / 2.6：
Conclusions / 2.7：
Anomalous Behavior Detection: Supporting Independent Living / 3：
Related work / 3.1：
Methodology / 3.3：
Unsupervised Classification Techniques / 3.3.1：
Using HMM to Model Behavior / 3.3.2：
Experimental Setup and Data Collection / 3.4：
Noisy Data: Sources of Error / 3.4.1：
Learning activities / 3.4.2：
Experimental Results / 3.5：
Instance Class Annotation / 3.5.1：
Data Preprocessing / 3.5.2：
Models: Unsupervised Classification: Clustering and Allocation of Activities to Clusters / 3.5.3：
Behaviors: Discovering Patterns in Activities / 3.5.4：
Behaviors: Discovering Anomalous Patterns of Activity / 3.5.5：
Discussion / 3.6：
Sequential Pattern Mining for Cooking-Support Robot / Yasushi Nakauchi3.7：
System Design / 4.1：
Inference from Series of Human Actions / 4.2.1：
Time Sequence Data Mining / 4.2.2：
Human Behavior Inference Algorithm / 4.2.3：
Activity Support of Human / 4.2.4：
Implementation / 4.3：
IC Tag System / 4.3.1：
Inference of Human's Next Action / 4.3.2：
Cooking Support Interface / 4.3.3：
Robotic, Sensory and Problem-Solving Ingredients for the Future Home / Amedeo Cesta ; Luca Iocchi ; G. Riccardo Leone ; Daniele Nardi ; Federico Pecora ; Riccardo Rasconi4.4：
Components of the Multiagent System / 5.1：
The Robotic Platform Mobility Subsystem / 5.2：
The Interaction Manager / 5.3：
Environmental Sensors for People Tracking and Posture Recognition / 5.4：
Monitoring Activities of Daily Living / 5.5：
Schedule Representation and Execution Monitoring / 5.5.1：
Constraint Management in the RoboCare Context / 5.5.2：
From Constraint Violations to Verbal Interaction / 5.5.3：
Multiagent Coordination Infrastructure / 5.6：
Casting the MAC Problem to DCOP / 5.6.1：
Cooperatively Solving the MAC Problem / 5.6.2：
Ubiquitous Stereo Vision for Human Sensing / Ikushi Yoda ; Katsuhiko Sakae5.7：
Ubiquitous Stereo Vision / 6.1：
Concept of Ubiquitous Stereo Vision / 6.2.1：
Server-Client Model for USV / 6.2.2：
Real Utilization Cases / 6.2.3：
Hierarchical Utilization of 3D Data and Personal Recognition / 6.3：
Acquisition of 3D Range Information / 6.3.1：
Projection to Floor Plane / 6.3.2：
Recognition of Multiple Persons and Interface / 6.4：
Pose Recognition for Multiple People / 6.4.1：
Personal Identification / 6.4.2：
Interface for Space Control / 6.4.3：
Human Monitoring in Open Space (Safety Management Application) / 6.5：
Monitoring Railroad Crossing / 6.5.1：
Station Platform Edge Safety Management / 6.5.2：
Monitoring Huge Space / 6.5.3：
Conclusion and Future Work / 6.6：
Augmenting Professional Training, an Ambient Intelligence Approach / B. Zhan ; D.N. Monekosso ; S. Rush ; P. Remagnino ; S.A. Velastin7：
Color Tracking of People / 7.1：
Counting People by Spatial Relationship Analysis / 7.3：
Simple People Counting Algorithm / 7.3.1：
Graphs of Blobs / 7.3.2：
Estimation of Distance Between Blobs / 7.3.3：
Temporal Pyramid for Distance Estimation / 7.3.4：
Probabilistic Estimation of Groupings / 7.3.5：
Grouping Blobs / 7.3.6：
Stereo Omnidirectional System (SOS) and Its Applications / Yutaka Satoh ; Katsuhiko Sakaue7.4：
System Configuration / 8.1：
Image integration / 8.3：
Generation of Stable Images at Arbitrary Rotation / 8.4：
An example Application: Intelligent Electric Wheelchair / 8.5：
Overview / 8.5.1：
Obstacle Detection / 8.5.2：
Gesture / Posture Detection / 8.5.4：
Video Analysis for Ambient Intelligence in Urban Environments / Andrea Prati ; Rita Cucchiara8.6：
Visual Data for Urban AmI / 9.1：
Video Surveillance in Urban Environment / 9.2.1：
The LAICA Project / 9.2.2：
Automatic Video Processing for People Tracking / 9.3：
People Detection and Tracking from Single Static Camera / 9.3.1：
People Detection and Tracking from Distributed Cameras / 9.3.2：
People Detection and Tracking from Moving Cameras / 9.3.3：
Privacy and Ethical Issues / 9.4：
From Monomodal to Multimodal: Affect Recognition Using Visual Modalities / Hatice Gunes ; Massimo Piccardi10：
Organization of the Chapter / 10.1：
From Monomodal to Multimodal: Changes and Challenges / 10.3：
Background Research / 10.3.1：
Data Collection / 10.3.2：
Data Annotation / 10.3.3：
Synchrony/Asynchrony Between Modalities / 10.3.4：
Data Integration/Fusion / 10.3.5：
Information Complementarity/Redundancy / 10.3.6：
Information Content of Modalities / 10.3.7：
Monomodal Systems Recognizing Affective Face or Body Movement / 10.4：
Multimodal Systems Recognizing Affect from Face and Body Movement / 10.5：
Project 1: Multimodal Affect Analysis for Future Cars / 10.5.1：
Project 2: Emotion Analysis in Man-Machine Interaction Systems / 10.5.2：
Project 3: Multimodal Affect Recognition in Learning Environments / 10.5.3：
Project 4: FABO-Fusing Face and Body Gestures for Bimodal Emotion Recognition / 10.5.4：
Multimodal Affect Systems: The Future / 10.6：
Importance of Vision in Human-Robot Communication: Understanding Speech Using Robot Vision and Demonstrating Proper Actions to Human Vision / Michie Kawashima ; Keiichi Yamazaki ; Akiko Yamazaki11：
Understanding Simplified Utterances Using Robot Vision / 11.1：
Inexplicit Utterances / 11.2.1：
Information Obtained by Vision / 11.2.2：
Language Processing / 11.2.3：
Vision Processing / 11.2.4：
Synchronization Between Speech and Vision / 11.2.5：
Experiments / 11.2.6：
Communicative Head Gestures for Museum Guide Robots / 11.3：
Observations from Guide-Visitor Interaction / 11.3.1：
Prototype Museum Guide Robot / 11.3.2：
Experiments at a Museum / 11.3.3：
Index / 11.4：
Preface
List of Contributors
Intelligent Environments: Methods, Algorithms and Applications / Dorothy N. Monekosso ; Paolo Remagnino ; Yoshinori Kuno1：
48.

電子ブック
ＥＢ
48. Intelligent Environments
Yoshinori Kuno., Yoshinori Kuno, Dorothy Monekosso, Paolo Remagnino
出版情報: SpringerLink Books - AutoHoldings , Springer London, 2009
所蔵情報: loading…
目次情報: 続きを見る
Preface
List of Contributors
Intelligent Environments: Methods, Algorithms and Applications / Dorothy N. Monekosso ; Paolo Remagnino ; Yoshinori Kuno1：
Intelligent Environments / 1.1：
What Is An Intelligent Environment? / 1.1.1：
How Is An Intelligent Environment Built? / 1.1.2：
Technology for Intelligent Environments / 1.2：
Research Projects / 1.3：
Private Spaces / 1.3.1：
Public Spaces / 1.3.2：
Middleware / 1.3.3：
Chapter Themes in This Collection / 1.4：
Conclusion / 1.5：
References
A Pervasive Sensor System for Evidence-Based Nursing Care Support / Toshio Hori ; Yoshifumi Nishida ; Shin'ichi Murakami2：
Introduction / 2.1：
Evidence-Based Nursing Care Support / 2.2：
Background of the Project / 2.2.1：
Concept of Evidence-Based Nursing Care Support / 2.2.2：
Initial Goal of the Project: Falls Prevention / 2.2.3：
Second Goal of the Project: Obtaining ADL of Inhabitants / 2.2.4：
Related Work / 2.3：
Overview and Implementations of the System / 2.4：
Overview of the Evidence-Based Nursing Care Support System / 2.4.1：
System Implementations / 2.4.2：
Experiments and Analyses / 2.5：
Tracking a Wheelchair for Falls Prevention / 2.5.1：
Activity Transition Diagram: Transition of Activities in One Day / 2.5.2：
Quantitative Evaluation of Daily Activities / 2.5.3：
Probability of "Toilet" Activity / 2.5.4：
Discussion of the Experimental Results / 2.5.5：
Prospect of the Evidence-Based Nursing Care Support System / 2.6：
Conclusions / 2.7：
Anomalous Behavior Detection: Supporting Independent Living / 3：
Related work / 3.1：
Methodology / 3.3：
Unsupervised Classification Techniques / 3.3.1：
Using HMM to Model Behavior / 3.3.2：
Experimental Setup and Data Collection / 3.4：
Noisy Data: Sources of Error / 3.4.1：
Learning activities / 3.4.2：
Experimental Results / 3.5：
Instance Class Annotation / 3.5.1：
Data Preprocessing / 3.5.2：
Models: Unsupervised Classification: Clustering and Allocation of Activities to Clusters / 3.5.3：
Behaviors: Discovering Patterns in Activities / 3.5.4：
Behaviors: Discovering Anomalous Patterns of Activity / 3.5.5：
Discussion / 3.6：
Sequential Pattern Mining for Cooking-Support Robot / Yasushi Nakauchi3.7：
System Design / 4.1：
Inference from Series of Human Actions / 4.2.1：
Time Sequence Data Mining / 4.2.2：
Human Behavior Inference Algorithm / 4.2.3：
Activity Support of Human / 4.2.4：
Implementation / 4.3：
IC Tag System / 4.3.1：
Inference of Human's Next Action / 4.3.2：
Cooking Support Interface / 4.3.3：
Robotic, Sensory and Problem-Solving Ingredients for the Future Home / Amedeo Cesta ; Luca Iocchi ; G. Riccardo Leone ; Daniele Nardi ; Federico Pecora ; Riccardo Rasconi4.4：
Components of the Multiagent System / 5.1：
The Robotic Platform Mobility Subsystem / 5.2：
The Interaction Manager / 5.3：
Environmental Sensors for People Tracking and Posture Recognition / 5.4：
Monitoring Activities of Daily Living / 5.5：
Schedule Representation and Execution Monitoring / 5.5.1：
Constraint Management in the RoboCare Context / 5.5.2：
From Constraint Violations to Verbal Interaction / 5.5.3：
Multiagent Coordination Infrastructure / 5.6：
Casting the MAC Problem to DCOP / 5.6.1：
Cooperatively Solving the MAC Problem / 5.6.2：
Ubiquitous Stereo Vision for Human Sensing / Ikushi Yoda ; Katsuhiko Sakae5.7：
Ubiquitous Stereo Vision / 6.1：
Concept of Ubiquitous Stereo Vision / 6.2.1：
Server-Client Model for USV / 6.2.2：
Real Utilization Cases / 6.2.3：
Hierarchical Utilization of 3D Data and Personal Recognition / 6.3：
Acquisition of 3D Range Information / 6.3.1：
Projection to Floor Plane / 6.3.2：
Recognition of Multiple Persons and Interface / 6.4：
Pose Recognition for Multiple People / 6.4.1：
Personal Identification / 6.4.2：
Interface for Space Control / 6.4.3：
Human Monitoring in Open Space (Safety Management Application) / 6.5：
Monitoring Railroad Crossing / 6.5.1：
Station Platform Edge Safety Management / 6.5.2：
Monitoring Huge Space / 6.5.3：
Conclusion and Future Work / 6.6：
Augmenting Professional Training, an Ambient Intelligence Approach / B. Zhan ; D.N. Monekosso ; S. Rush ; P. Remagnino ; S.A. Velastin7：
Color Tracking of People / 7.1：
Counting People by Spatial Relationship Analysis / 7.3：
Simple People Counting Algorithm / 7.3.1：
Graphs of Blobs / 7.3.2：
Estimation of Distance Between Blobs / 7.3.3：
Temporal Pyramid for Distance Estimation / 7.3.4：
Probabilistic Estimation of Groupings / 7.3.5：
Grouping Blobs / 7.3.6：
Stereo Omnidirectional System (SOS) and Its Applications / Yutaka Satoh ; Katsuhiko Sakaue7.4：
System Configuration / 8.1：
Image integration / 8.3：
Generation of Stable Images at Arbitrary Rotation / 8.4：
An example Application: Intelligent Electric Wheelchair / 8.5：
Overview / 8.5.1：
Obstacle Detection / 8.5.2：
Gesture / Posture Detection / 8.5.4：
Video Analysis for Ambient Intelligence in Urban Environments / Andrea Prati ; Rita Cucchiara8.6：
Visual Data for Urban AmI / 9.1：
Video Surveillance in Urban Environment / 9.2.1：
The LAICA Project / 9.2.2：
Automatic Video Processing for People Tracking / 9.3：
People Detection and Tracking from Single Static Camera / 9.3.1：
People Detection and Tracking from Distributed Cameras / 9.3.2：
People Detection and Tracking from Moving Cameras / 9.3.3：
Privacy and Ethical Issues / 9.4：
From Monomodal to Multimodal: Affect Recognition Using Visual Modalities / Hatice Gunes ; Massimo Piccardi10：
Organization of the Chapter / 10.1：
From Monomodal to Multimodal: Changes and Challenges / 10.3：
Background Research / 10.3.1：
Data Collection / 10.3.2：
Data Annotation / 10.3.3：
Synchrony/Asynchrony Between Modalities / 10.3.4：
Data Integration/Fusion / 10.3.5：
Information Complementarity/Redundancy / 10.3.6：
Information Content of Modalities / 10.3.7：
Monomodal Systems Recognizing Affective Face or Body Movement / 10.4：
Multimodal Systems Recognizing Affect from Face and Body Movement / 10.5：
Project 1: Multimodal Affect Analysis for Future Cars / 10.5.1：
Project 2: Emotion Analysis in Man-Machine Interaction Systems / 10.5.2：
Project 3: Multimodal Affect Recognition in Learning Environments / 10.5.3：
Project 4: FABO-Fusing Face and Body Gestures for Bimodal Emotion Recognition / 10.5.4：
Multimodal Affect Systems: The Future / 10.6：
Importance of Vision in Human-Robot Communication: Understanding Speech Using Robot Vision and Demonstrating Proper Actions to Human Vision / Michie Kawashima ; Keiichi Yamazaki ; Akiko Yamazaki11：
Understanding Simplified Utterances Using Robot Vision / 11.1：
Inexplicit Utterances / 11.2.1：
Information Obtained by Vision / 11.2.2：
Language Processing / 11.2.3：
Vision Processing / 11.2.4：
Synchronization Between Speech and Vision / 11.2.5：
Experiments / 11.2.6：
Communicative Head Gestures for Museum Guide Robots / 11.3：
Observations from Guide-Visitor Interaction / 11.3.1：
Prototype Museum Guide Robot / 11.3.2：
Experiments at a Museum / 11.3.3：
Index / 11.4：
Preface
List of Contributors
Intelligent Environments: Methods, Algorithms and Applications / Dorothy N. Monekosso ; Paolo Remagnino ; Yoshinori Kuno1：
49.

電子ブック
ＥＢ
49. Logical and Relational Learning
Luc De Raedt, Jörg Siekmann, A. Bundy
出版情報: SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2008
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Introduction / 1：
What Is Logical and Relational Learning? / 1.1：
Why Is Logical and Relational Learning Important? / 1.2：
Structure Activity Relationship Prediction / 1.2.1：
A Web Mining Example / 1.2.2：
A Language Learning Example / 1.2.3：
How Does Relational and Logical Learning Work? / 1.3：
A Brief History / 1.4：
An Introduction to Logic / 2：
A Relational Database Example / 2.1：
The Syntax of Clausal Logic / 2.2：
The Semantics of Clausal Logic - Model Theory / 2.3：
Inference with Clausal Logic - Proof Theory / 2.4：
Prolog and SLD-resolution / 2.5：
Historical and Bibliographic Remarks / 2.6：
An Introduction to Learning and Search / 3：
Representing Hypotheses and Instances / 3.1：
Boolean Data / 3.2：
Machine Learning / 3.3：
Data Mining / 3.4：
A Generate-and-Test Algorithm / 3.5：
Structuring the Search Space / 3.6：
Monotonicity / 3.7：
Borders / 3.8：
Refinement Operators / 3.9：
A Generic Algorithm for Mining and Learning / 3.10：
A Complete General-to-Specific Algorithm / 3.11：
A Heuristic General-to-Specific Algorithm / 3.12：
A Branch-and-Bound Algorithm / 3.13：
A Specific-to-General Algorithm / 3.14：
Working with Borders* / 3.15：
Computing a Single Border / 3.15.1：
Computing Two Borders / 3.15.2：
Computing Two Borders Incrementally / 3.15.3：
Operations on Borders / 3.15.4：
Conclusions / 3.16：
Bibliographical Notes / 3.17：
Representations for Mining and Learning / 4：
Representing Data and Hypotheses / 4.1：
Attribute-Value Learning / 4.2：
Multiple-Instance Learning: Dealing With Sets / 4.3：
Relational Learning / 4.4：
Logic Programs / 4.5：
Sequences, Lists, and Grammars / 4.6：
Trees and Terms / 4.7：
Graphs / 4.8：
Background Knowledge / 4.9：
Designing It Yourself / 4.10：
A Hierarchy of Representations* / 4.11：
From AV to BL / 4.11.1：
From MI to AV / 4.11.2：
From RL to MI / 4.11.3：
From LP to RL / 4.11.4：
Propositionalization / 4.12：
A Table-Based Approach / 4.12.1：
A Query-Based Approach / 4.12.2：
Aggregation / 4.13：
Historical and Bibliographical Remarks / 4.14：
Generality and Logical Entailment / 5：
Generality and Logical Entailment Coincide / 5.1：
Propositional Subsumption / 5.2：
Subsumption in Logical Atoms / 5.3：
Specialization Operators / 5.3.1：
Generalization Operators* / 5.3.2：
Computing the lgg and the glb / 5.3.3：
[Theta]-Subsumption / 5.4：
Soundness and Completeness / 5.4.1：
Deciding [Theta]-Subsumption / 5.4.2：
Equivalence Classes / 5.4.3：
Variants of [Theta]-Subsumption* / 5.5：
Object Identity* / 5.5.1：
Inverse Implication* / 5.5.2：
Using Background Knowledge / 5.6：
Saturation and Bottom Clauses / 5.6.1：
Relative Least General Generalization* / 5.6.2：
Semantic Refinement* / 5.6.3：
Aggregation* / 5.7：
Inverse Resolution / 5.8：
A Note on Graphs, Trees, and Sequences / 5.9：
Bibliographic Notes / 5.10：
The Upgrading Story / 6：
Motivation for a Methodology / 6.1：
Methodological Issues / 6.2：
Representing the Examples / 6.2.1：
Representing the Hypotheses / 6.2.2：
Adapting the Algorithm / 6.2.3：
Adding Features / 6.2.4：
Case Study 1: Rule Learning and Foil / 6.3：
Foil's Problem Setting / 6.3.1：
Foil's Algorithm / 6.3.2：
Case Study 2: Decision Tree Learning and Tilde / 6.4：
The Problem Setting / 6.4.1：
Inducing Logical Decision Trees / 6.4.2：
Case Study 3: Frequent Item-Set Mining and Warmr / 6.5：
Relational Association Rules and Local Patterns / 6.5.1：
Computing Frequent Queries / 6.5.2：
Language Bias / 6.6：
Syntactic Bias / 6.6.1：
Semantic Bias / 6.6.2：
Inducing Theories / 6.7：
Introduction to Theory Revision / 7.1：
Theories and Model Inference / 7.1.1：
Theory Revision / 7.1.2：
Overview of the Rest of This Chapter / 7.1.3：
Towards Abductive Logic Programming / 7.2：
Abduction / 7.2.1：
Integrity Constraints / 7.2.2：
Abductive Logic Programming / 7.2.3：
Shapiro's Theory Revision System / 7.3：
Interaction / 7.3.1：
The Model Inference System / 7.3.2：
Two Propositional Theory Revision Systems* / 7.4：
Learning a Propositional Horn Theory Efficiently / 7.4.1：
Heuristic Search in Theory Revision / 7.4.2：
Inducing Constraints / 7.5：
Problem Specification / 7.5.1：
An Algorithm for Inducing Integrity Constraints / 7.5.2：
Probabilistic Logic Learning / 7.6：
Probability Theory Review / 8.1：
Probabilistic Logics / 8.2：
Probabilities on Interpretations / 8.2.1：
Probabilities on Proofs / 8.2.2：
Probabilistic Learning / 8.3：
Parameter Estimation / 8.3.1：
Structure Learning / 8.3.2：
First-Order Probabilistic Logics / 8.4：
Probabilistic Interpretations / 8.4.1：
Probabilistic Proofs / 8.4.2：
Learning from Interpretations / 8.5：
Learning from Entailment / 8.5.2：
Learning from Proof Trees and Traces / 8.5.3：
Relational Reinforcement Learning* / 8.6：
Markov Decision Processes / 8.6.1：
Solving Markov Decision Processes / 8.6.2：
Relational Markov Decision Processes / 8.6.3：
Solving Relational Markov Decision Processes / 8.6.4：
Kernels and Distances for Structured Data / 8.7：
A Simple Kernel and Distance / 9.1：
Kernel Methods / 9.2：
The Max Margin Approach / 9.2.1：
Support Vector Machines / 9.2.2：
The Kernel Trick / 9.2.3：
Distance-Based Learning / 9.3：
Distance Functions / 9.3.1：
The k-Nearest Neighbor Algorithm / 9.3.2：
The k-Means Algorithm / 9.3.3：
Kernels for Structured Data / 9.4：
Convolution and Decomposition / 9.4.1：
Vectors and Tuples / 9.4.2：
Sets and Multi-sets / 9.4.3：
Strings / 9.4.4：
Trees and Atoms / 9.4.5：
Graph Kernels* / 9.4.6：
Distances and Metrics / 9.5：
Generalization and Metrics / 9.5.1：
Sets / 9.5.2：
Atoms and Trees / 9.5.4：
Relational Kernels and Distances / 9.5.6：
Bibliographical and Historical Notes / 9.7：
Computational Aspects of Logical and Relational Learning / 10：
Efficiency of Relational Learning / 10.1：
Coverage as [theta]-Subsumption / 10.1.1：
[theta]-Subsumption Empirically / 10.1.2：
Optimizing the Learner for [theta]-subsumption / 10.1.3：
Computational Learning Theory* / 10.2：
Notions of Learnability / 10.2.1：
Positive Results / 10.2.2：
Negative Results / 10.2.3：
Historical and Bibliographic Notes / 10.3：
Lessons Learned / 11：
A Hierarchy of Representations / 11.1：
From Upgrading to Downgrading / 11.2：
Propositionalization and Aggregation / 11.3：
Learning Tasks / 11.4：
Operators and Generality / 11.5：
Unification and Variables / 11.6：
Three Learning Settings / 11.7：
Knowledge and Background Knowledge / 11.8：
Applications / 11.9：
References
Author Index
Index
Introduction / 1：
What Is Logical and Relational Learning? / 1.1：
Why Is Logical and Relational Learning Important? / 1.2：
50.

電子ブック
ＥＢ
50. Fundamentals of the New Artificial Intelligence
Toshinori Munakata, David Gries, Fred B. Schneider
出版情報: Springer eBooks Computer Science , Springer London, 2008
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Preface
Introduction / 1：
An Overview of the Field of Artificial Intelligence / 1.1：
An Overview of the Areas Covered in this Book / 1.2：
Neural Networks: Fundamentals and the Backpropagation Model / 2：
What is a Neural Network? / 2.1：
A Neuron / 2.2：
Basic Idea of the Backpropagation Model / 2.3：
Details of the Backpropagation Mode / 2.4：
A Cookbook Recipe to Implement the Backpropagation Model / 2.5：
Additional Technical Remarks on the Backpropagation Model / 2.6：
Simple Perceptrons / 2.7：
Applications of the Backpropagation Model / 2.8：
General Remarks on Neural Networks / 2.9：
Neural Networks: Other Models / 3：
Prelude / 3.1：
Associative Memory / 3.2：
Hopfield Networks / 3.3：
The Hopfield-Tank Model for Optimization Problems: The Basics / 3.4：
One-Dimensional Layout / 3.4.1：
Two-Dimensional Layout / 3.4.2：
The Hopfield-Tank Model for Optimization Problems: Applications / 3.5：
The N-Queen Problem / 3.5.1：
A General Guideline to Apply the Hopfield-Tank Model to Optimization Problems / 3.5.2：
Traveling Salesman Problem (TSP) / 3.5.3：
The Kohonen Model / 3.6：
Simulated Annealing / 3.7：
Boltzmann Machines / 3.8：
An Overview / 3.8.1：
Unsupervised Learning by the Boltzmann Machine: The Basics Architecture / 3.8.2：
Unsupervised Learning by the Boltzmann Machine: Algorithms / 3.8.3：
Appendix. Derivation of Delta-Weights / 3.8.4：
Genetic Algorithms and Evolutionary Computing / 4：
What are Genetic Algorithms and Evolutionary Computing? / 4.1：
Fundamentals of Genetic Algorithms / 4.2：
A Simple Illustration of Genetic Algorithms / 4.3：
A Machine Learning Example: Input-to-Output Mapping / 4.4：
A Hard Optimization Example: the Traveling Salesman Problem (TSP) / 4.5：
Schemata / 4.6：
Changes of Schemata Over Generations / 4.6.1：
Example of Schema Processing / 4.6.2：
Genetic Programming / 4.7：
Additional Remarks / 4.8：
Fuzzy Systems / 5：
Fundamentals of Fuzzy Sets / 5.1：
What is a Fuzzy Set? / 5.2.1：
Basic Fuzzy Set Relations / 5.2.2：
Basic Fuzzy Set Operations and Their Properties / 5.2.3：
Operations Unique to Fuzzy Sets / 5.2.4：
Fuzzy Relations / 5.3：
Ordinary (Nonfuzzy) Relations / 5.3.1：
Fuzzy Relations Defined on Ordinary Sets / 5.3.2：
Fuzzy Relations Derived from Fuzzy Sets / 5.3.3：
Fuzzy Logic / 5.4：
Ordinary Set Theory and Ordinary Logic / 5.4.1：
Fuzzy Logic Fundamentals / 5.4.2：
Fuzzy Control / 5.5：
Fuzzy Control Basics / 5.5.1：
Case Study: Controlling Temperature with a Variable Heat Source / 5.5.2：
Extended Fuzzy if-then Rules Tables / 5.5.3：
A Note on Fuzzy Control Expert Systems / 5.5.4：
Hybrid Systems / 5.6：
Fundamental Issues / 5.7：
Rough Sets / 5.8：
Review of Ordinary Sets and Relations / 6.1：
Information Tables and Attributes / 6.3：
Approximation Spaces / 6.4：
Knowledge Representation Systems / 6.5：
More on the Basics of Rough Sets / 6.6：
Case Study and Comparisons with Other Techniques / 6.7：
Rough Sets Applied to the Case Study / 6.8.1：
ID3 Approach and the Case Study / 6.8.2：
Comparisons with Other Techniques / 6.8.3：
Chaos / 7：
What is Chaos? / 7.1：
Representing Dynamical Systems / 7.2：
Discrete dynamical systems / 7.2.1：
Continuous dynamical systems / 7.2.2：
State and Phase Spaces / 7.3：
Trajectory, Orbit and Flow / 7.3.1：
Cobwebs / 7.3.2：
Equilibrium Solutions and Stability / 7.4：
Attractors / 7.5：
Fixed-point attractors / 7.5.1：
Periodic attractors / 7.5.2：
Quasi-periodic attractors / 7.5.3：
Chaotic attractors / 7.5.4：
Bifurcations / 7.6：
Fractals / 7.7：
Applications of Chaos / 7.8：
Index
Preface
Introduction / 1：
An Overview of the Field of Artificial Intelligence / 1.1：
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