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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
子書誌情報:	loading…
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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：

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
子書誌情報:	loading…
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：

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
子書誌情報:	loading…
所蔵情報:	loading…

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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：

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
子書誌情報:	loading…
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：

電子ブック

ＥＢ

5. 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
子書誌情報:	loading…
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：

電子ブック

ＥＢ

6. 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
子書誌情報:	loading…
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：

電子ブック

ＥＢ

7. 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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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：

電子ブック

ＥＢ

8. Pedagogically Founded Courseware Generation for Web-Based Learning

Carsten Ullrich, Jörg Siekmann

出版情報:	SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2008
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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：

電子ブック

ＥＢ

9. 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 / 1.11：

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：

15.

電子ブック

ＥＢ

15. 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：

16.

電子ブック

ＥＢ

16. 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
子書誌情報:	loading…
所蔵情報:	loading…

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：

17.

電子ブック

ＥＢ

17. 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：

18.

電子ブック

ＥＢ

18. The Path to Autonomous Robots

Gaurav Sukhatme, Gaurav S. Sukhatme

出版情報:	SpringerLink Books - AutoHoldings , Springer US, 2009
子書誌情報:	loading…
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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：

19.

電子ブック

ＥＢ

19. 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：

20.

電子ブック

ＥＢ

20. Bee-Inspired Protocol Engineering

Muddassar Farooq, Th Bäck, A. E. Eiben, G. Rozenberg

出版情報:	SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2009
子書誌情報:	loading…
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：

21.

電子ブック

ＥＢ

21. Robots, Reasoning, and Reification

James P. Gunderson, Louise F. Gunderson, Unspecified

出版情報:	Springer eBooks Computer Science , Springer US, 2009
子書誌情報:	loading…
所蔵情報:	loading…

目次情報: 続きを見る

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：

22.

電子ブック

ＥＢ

22. 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：

23.

電子ブック

ＥＢ

23. Robots, Reasoning, and Reification

James P. Gunderson, Louise F. Gunderson, Unspecified

出版情報:	SpringerLink Books - AutoHoldings , Springer US, 2009
子書誌情報:	loading…
所蔵情報:	loading…

目次情報: 続きを見る

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：

24.

電子ブック

ＥＢ

24. Understanding Planning Tasks

Malte Helmert, Jörg Siekmann

出版情報:	SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2008
子書誌情報:	loading…
所蔵情報:	loading…

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：

25.

電子ブック

ＥＢ

25. 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

26.

電子ブック

ＥＢ

26. 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

27.

電子ブック

ＥＢ

27. 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：

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：

31.

電子ブック

ＥＢ

31. 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：

32.

電子ブック

ＥＢ

32. 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：

33.

電子ブック

ＥＢ

33. Intelligent Environments

Yoshinori Kuno., Yoshinori Kuno, Dorothy Monekosso, Paolo Remagnino

出版情報:	SpringerLink Books - AutoHoldings , Springer London, 2009
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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：

34.

電子ブック

ＥＢ

34. 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：

35.

電子ブック

ＥＢ

35. 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：

36.

電子ブック

ＥＢ

36. Fundamentals of the New Artificial Intelligence

Toshinori Munakata, David Gries, Fred B. Schneider

出版情報:	SpringerLink Books - AutoHoldings , 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：

37.

電子ブック

ＥＢ

37. Robot Cognition and Navigation

Srikanta Patnaik, D.M Gabbay, J. Siekmann

出版情報:	Springer eBooks Computer Science , Springer Berlin Heidelberg, 2007
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Cybernetic View of Robot Cognition and Perception / 1：

Introduction to the Model of Cognition / 1.1：

Various States of Cognition / 1.1.1：

Cycles of Cognition / 1.1.2：

Visual Perception / 1.2：

Human Visual System / 1.2.1：

Vision for Mobile Robots / 1.2.2：

Visual Recognition / 1.3：

Template Matching / 1.3.1：

Feature-Based Model / 1.3.2：

Fourier Model / 1.3.3：

Structural Model / 1.3.4：

The Computational Theory of Marr / 1.3.5：

Machine Learning / 1.4：

Properties and Issues in Machine Learning / 1.4.1：

Classification of Machine Learning / 1.4.2：

Soft Computing Tools and Robot Cognition / 1.5：

Modeling Cognition Using ANN / 1.5.1：

Fuzzy Logic in Robot Cognition / 1.5.2：

Genetic Algorithms in Robot Cognition / 1.5.3：

Summary / 1.6：

Map Building / 2：

Introduction / 2.1：

Constructing a 2D World Map / 2.2：

Data Structure for Map Building / 2.2.1：

Explanation of the Algorithm / 2.2.2：

An Illustration of Procedure: Traverse Boundary / 2.2.3：

An Illustration of Procedure: Map Building / 2.2.4：

Robot Simulation / 2.2.5：

Execution of the Map Building Program / 2.3：

Path Planning / 2.4：

Representation of the Robot's Environment / 3.1：

GVD Using Cellular Automata / 3.2.1：

Path Optimization by the Quadtree Approach / 3.3：

Introduction to the Quadtree / 3.3.1：

Definition / 3.3.2：

Generation of the Quadtree / 3.3.3：

Neighbor-Finding Algorithms for the Quadtree / 3.4：

The A Algorithm for Selecting the Best Neighbor / 3.5：

Execution of the Quadtree-Based Path Planner Program / 3.6：

Navigation Using a Genetic Algorithm / 3.7：

Genetic Algorithms / 4.1：

Encoding of a Chromosome / 4.2.1：

Crossover / 4.2.2：

Mutation / 4.2.3：

Parameters of a GA / 4.2.4：

Selection / 4.2.5：

Navigation by a Genetic Algorithm / 4.3：

Formulation of Navigation / 4.3.1：

Execution of the GA-Based Navigation Program / 4.4：

Replanning by Temporal Associative Memory / 4.5：

Introduction to TAM / 4.5.1：

Encoding and Decoding Process in a Temporal Memory / 4.5.2：

An Example in a Semi-dynamic Environment / 4.5.3：

Implications of Results / 4.5.4：

Robot Programming Packages / 4.6：

Robot Hardware and Software Resources / 5.1：

Components / 5.2.1：

ARIA / 5.3：

ARIA Client-Server / 5.3.1：

Robot Communication / 5.3.2：

Opening the Connection / 5.3.3：

ArRobot / 5.3.4：

Range Devices / 5.3.5：

Commands and Actions / 5.3.6：

Socket Programming / 5.4：

Socket Programming in ARIA / 5.4.1：

BotSpeak Speech System / 5.5：

Functions / 5.5.1：

Small Vision System (SVS) / 5.6：

SVS C++ Classes / 5.6.1：

Parameter Classes / 5.6.2：

Stereo Image Class / 5.6.3：

Acquisition Classes / 5.6.4：

Multithreading / 5.7：

Client Front-End Design Using Java / 5.8：

Robot Parameter Display / 5.9：

Flow Chart and Source Code for Robot Parameter Display / 6.1：

Program for BotSpeak / 6.3：

Flow Chart and Source Code for BotSpeak Program / 7.1：

Gripper Control Program / 7.3：

Flow Chart and Source Code for Gripper Control Program / 8.1：

Program for Sonar Reading Display / 8.3：

Flow Chart and Source Code for Sonar Reading Display on Client / 9.1：

Program for Wandering Within the Workspace / 9.3：

Algorithm and Source Code for Wandering Within the Workspace / 10.1：

Program for Tele-operation / 10.3：

Algorithm and Source Code for Tele-operation / 11.1：

A Complete Program for Autonomous Navigation / 11.3：

The ImageServer Program / 12.1：

The MotionServer Program / 12.3：

The Navigator Client Program / 12.4：

Imaging Geometry / 12.5：

Necessity for 3D Reconstruction / 13.1：

Building Perception / 13.3：

Problems of Understanding 3D Objects from 2D Imagery / 13.3.1：

Process of 3D Reconstruction / 13.3.2：

Image Formation / 13.4：

Perspective Projection in One Dimension / 13.4.2：

Perspective Projection in 3D / 13.4.3：

Global Representation / 13.5：

Transformation to Global Coordinate System / 13.6：

Image Capture Program / 13.7：

Algorithm for Image Capture / 14.1：

Building 3D Perception Using a Kalman Filter / 14.3：

Minimal Representation / 15.1：

Recursive Kalman Filter / 15.3：

Experiments and Estimation / 15.4：

Reconstruction of 3D Points / 15.4.1：

Reconstruction of a 3D Line / 15.4.2：

Reconstruction of a 3D Plane / 15.4.3：

Correspondence Problem in 3D Recovery / 15.5：

Program for 3D Perception / 15.6：

Flow Chart and Source Code for 3D Perception / 16.1：

Perceptions of Non-planar Surfaces / 16.3：

Methods of Edge Detection / 17.1：

Curve Tracking and Curve Fitting / 17.3：

Program for Curve Detector / 17.4：

Intelligent Garbage Collection / 17.5：

Algorithms and Source Code for Garbage Collection / 18.1：

References / 18.3：

Index

Cybernetic View of Robot Cognition and Perception / 1：

Introduction to the Model of Cognition / 1.1：

Various States of Cognition / 1.1.1：

38.

電子ブック

ＥＢ

38. Robot Cognition and Navigation

Srikanta Patnaik, D.M Gabbay, J. Siekmann, Luigia Carlucci Aiello

出版情報:	SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2007
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Cybernetic View of Robot Cognition and Perception / 1：

Introduction to the Model of Cognition / 1.1：

Various States of Cognition / 1.1.1：

Cycles of Cognition / 1.1.2：

Visual Perception / 1.2：

Human Visual System / 1.2.1：

Vision for Mobile Robots / 1.2.2：

Visual Recognition / 1.3：

Template Matching / 1.3.1：

Feature-Based Model / 1.3.2：

Fourier Model / 1.3.3：

Structural Model / 1.3.4：

The Computational Theory of Marr / 1.3.5：

Machine Learning / 1.4：

Properties and Issues in Machine Learning / 1.4.1：

Classification of Machine Learning / 1.4.2：

Soft Computing Tools and Robot Cognition / 1.5：

Modeling Cognition Using ANN / 1.5.1：

Fuzzy Logic in Robot Cognition / 1.5.2：

Genetic Algorithms in Robot Cognition / 1.5.3：

Summary / 1.6：

Map Building / 2：

Introduction / 2.1：

Constructing a 2D World Map / 2.2：

Data Structure for Map Building / 2.2.1：

Explanation of the Algorithm / 2.2.2：

An Illustration of Procedure: Traverse Boundary / 2.2.3：

An Illustration of Procedure: Map Building / 2.2.4：

Robot Simulation / 2.2.5：

Execution of the Map Building Program / 2.3：

Path Planning / 2.4：

Representation of the Robot's Environment / 3.1：

GVD Using Cellular Automata / 3.2.1：

Path Optimization by the Quadtree Approach / 3.3：

Introduction to the Quadtree / 3.3.1：

Definition / 3.3.2：

Generation of the Quadtree / 3.3.3：

Neighbor-Finding Algorithms for the Quadtree / 3.4：

The A Algorithm for Selecting the Best Neighbor / 3.5：

Execution of the Quadtree-Based Path Planner Program / 3.6：

Navigation Using a Genetic Algorithm / 3.7：

Genetic Algorithms / 4.1：

Encoding of a Chromosome / 4.2.1：

Crossover / 4.2.2：

Mutation / 4.2.3：

Parameters of a GA / 4.2.4：

Selection / 4.2.5：

Navigation by a Genetic Algorithm / 4.3：

Formulation of Navigation / 4.3.1：

Execution of the GA-Based Navigation Program / 4.4：

Replanning by Temporal Associative Memory / 4.5：

Introduction to TAM / 4.5.1：

Encoding and Decoding Process in a Temporal Memory / 4.5.2：

An Example in a Semi-dynamic Environment / 4.5.3：

Implications of Results / 4.5.4：

Robot Programming Packages / 4.6：

Robot Hardware and Software Resources / 5.1：

Components / 5.2.1：

ARIA / 5.3：

ARIA Client-Server / 5.3.1：

Robot Communication / 5.3.2：

Opening the Connection / 5.3.3：

ArRobot / 5.3.4：

Range Devices / 5.3.5：

Commands and Actions / 5.3.6：

Socket Programming / 5.4：

Socket Programming in ARIA / 5.4.1：

BotSpeak Speech System / 5.5：

Functions / 5.5.1：

Small Vision System (SVS) / 5.6：

SVS C++ Classes / 5.6.1：

Parameter Classes / 5.6.2：

Stereo Image Class / 5.6.3：

Acquisition Classes / 5.6.4：

Multithreading / 5.7：

Client Front-End Design Using Java / 5.8：

Robot Parameter Display / 5.9：

Flow Chart and Source Code for Robot Parameter Display / 6.1：

Program for BotSpeak / 6.3：

Flow Chart and Source Code for BotSpeak Program / 7.1：

Gripper Control Program / 7.3：

Flow Chart and Source Code for Gripper Control Program / 8.1：

Program for Sonar Reading Display / 8.3：

Flow Chart and Source Code for Sonar Reading Display on Client / 9.1：

Program for Wandering Within the Workspace / 9.3：

Algorithm and Source Code for Wandering Within the Workspace / 10.1：

Program for Tele-operation / 10.3：

Algorithm and Source Code for Tele-operation / 11.1：

A Complete Program for Autonomous Navigation / 11.3：

The ImageServer Program / 12.1：

The MotionServer Program / 12.3：

The Navigator Client Program / 12.4：

Imaging Geometry / 12.5：

Necessity for 3D Reconstruction / 13.1：

Building Perception / 13.3：

Problems of Understanding 3D Objects from 2D Imagery / 13.3.1：

Process of 3D Reconstruction / 13.3.2：

Image Formation / 13.4：

Perspective Projection in One Dimension / 13.4.2：

Perspective Projection in 3D / 13.4.3：

Global Representation / 13.5：

Transformation to Global Coordinate System / 13.6：

Image Capture Program / 13.7：

Algorithm for Image Capture / 14.1：

Building 3D Perception Using a Kalman Filter / 14.3：

Minimal Representation / 15.1：

Recursive Kalman Filter / 15.3：

Experiments and Estimation / 15.4：

Reconstruction of 3D Points / 15.4.1：

Reconstruction of a 3D Line / 15.4.2：

Reconstruction of a 3D Plane / 15.4.3：

Correspondence Problem in 3D Recovery / 15.5：

Program for 3D Perception / 15.6：

Flow Chart and Source Code for 3D Perception / 16.1：

Perceptions of Non-planar Surfaces / 16.3：

Methods of Edge Detection / 17.1：

Curve Tracking and Curve Fitting / 17.3：

Program for Curve Detector / 17.4：

Intelligent Garbage Collection / 17.5：

Algorithms and Source Code for Garbage Collection / 18.1：

References / 18.3：

Index

Cybernetic View of Robot Cognition and Perception / 1：

Introduction to the Model of Cognition / 1.1：

Various States of Cognition / 1.1.1：

39.

電子ブック

ＥＢ

39. Convergence and Knowledge Processing in Multi-Agent Systems

Maria Chli, Philippe De Wilde, Lakhmi Jain

出版情報:	Springer eBooks Computer Science , Springer London, 2009
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Introduction / 1：

Background to the Research / 1.1：

Approach / 1.2：

Contributions / 1.3：

Reader's Guide to the Book / 1.4：

Research Issues / 2：

Multi-agent Systems / 2.1：

Agent-based Modelling / 2.2：

An Ecosystem Perspective of Multi-agent Systems / 2.3：

Convergence Issues / 2.4：

Interaction and Knowledge Exchange / 2.5：

Stability of Multi-agent Systems / 3：

Background / 3.1：

Stability in Games / 3.3：

Stochastic Systems Primer / 3.3.1：

Definition of Stability / 3.3.2：

Example Games / 3.3.3：

Experiments / 3.4：

Trading Simulation Model / 3.4.1：

Load Transportation Model / 3.4.2：

Virus Spreading Model / 3.4.3：

The Market Demonstrator / 3.4.4：

Conclusion / 3.5：

Limitations and Future Work / 3.5.1：

Achievements / 3.5.2：

The Emergence of Knowledge Exchange: An Agent-based Model of a Software Market / 4：

Digital Business Ecosystem / 4.1：

A DBE Economy / 4.2.1：

Market Efficiency / 4.2.2：

Exchange in Economic Markets / 4.3：

The Software Industry / 4.3.2：

An Agent-based Model of the DBE / 4.4：

The Setting / 4.4.1：

Exchange of Services / 4.4.3：

Discussion / 4.4.4：

Analysis of the Model / 4.5：

Service Exchange / 4.5.1：

Concluding Remarks / 4.5.2：

Collaborative Query Expansion / 5：

Query Expansion / 5.1：

Discriminative Document Terms / 5.1.2：

Term Value / 5.2：

Implementation / 5.3：

Initial Phase / 5.3.1：

Stemming / 5.3.2：

Common Word Filtering / 5.3.3：

Term Selection / 5.3.4：

Evaluation / 5.4：

Evaluation Results / 5.4.1：

Introducing User Collaboration for Query Expansion / 5.5：

Collaboration Procedure / 5.5.1：

Comparing Sets of Terms / 5.5.2：

Example of Collaboration / 5.5.3：

Conclusions / 5.6：

Micro-economic Control of Distributed Intelligent Personal Assistants / 6：

Stable Strategies / 6.1：

Network of Intelligent Personal Assistants / 6.2：

Definition of the Automatic PA / 6.2.1：

Further Specifications / 6.2.2：

The Intelligent Automatic PA / 6.2.3：

Negotiating and Optimizing Agents / 6.2.4：

An Example / 6.2.5：

Finding a Stable Strategy / 6.3：

The Discrete Event Simulator / 6.3.1：

A Stable Strategy / 6.3.2：

Conclusions and Future Work / 6.4：

Future Directions / 7.1：

Ecosystems of Networked Businesses / 7.2.1：

Exchange in Natural Ecosystems / 7.2.2：

Appendices / 7.3：

The EEII Project / A：

Statistical Analysis / B：

Statistical Hypothesis Testing / B.1：

Tests for Showing That Two Samples Come from the Same Distribution / B.2：

Methodology: Evolutionary Algorithms / C：

References

Index

Introduction / 1：

Background to the Research / 1.1：

Approach / 1.2：

40.

電子ブック

ＥＢ

40. Convergence and Knowledge Processing in Multi-Agent Systems

Maria Chli, Philippe De Wilde, Lakhmi Jain

出版情報:	SpringerLink Books - AutoHoldings , Springer London, 2009
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Introduction / 1：

Background to the Research / 1.1：

Approach / 1.2：

Contributions / 1.3：

Reader's Guide to the Book / 1.4：

Research Issues / 2：

Multi-agent Systems / 2.1：

Agent-based Modelling / 2.2：

An Ecosystem Perspective of Multi-agent Systems / 2.3：

Convergence Issues / 2.4：

Interaction and Knowledge Exchange / 2.5：

Stability of Multi-agent Systems / 3：

Background / 3.1：

Stability in Games / 3.3：

Stochastic Systems Primer / 3.3.1：

Definition of Stability / 3.3.2：

Example Games / 3.3.3：

Experiments / 3.4：

Trading Simulation Model / 3.4.1：

Load Transportation Model / 3.4.2：

Virus Spreading Model / 3.4.3：

The Market Demonstrator / 3.4.4：

Conclusion / 3.5：

Limitations and Future Work / 3.5.1：

Achievements / 3.5.2：

The Emergence of Knowledge Exchange: An Agent-based Model of a Software Market / 4：

Digital Business Ecosystem / 4.1：

A DBE Economy / 4.2.1：

Market Efficiency / 4.2.2：

Exchange in Economic Markets / 4.3：

The Software Industry / 4.3.2：

An Agent-based Model of the DBE / 4.4：

The Setting / 4.4.1：

Exchange of Services / 4.4.3：

Discussion / 4.4.4：

Analysis of the Model / 4.5：

Service Exchange / 4.5.1：

Concluding Remarks / 4.5.2：

Collaborative Query Expansion / 5：

Query Expansion / 5.1：

Discriminative Document Terms / 5.1.2：

Term Value / 5.2：

Implementation / 5.3：

Initial Phase / 5.3.1：

Stemming / 5.3.2：

Common Word Filtering / 5.3.3：

Term Selection / 5.3.4：

Evaluation / 5.4：

Evaluation Results / 5.4.1：

Introducing User Collaboration for Query Expansion / 5.5：

Collaboration Procedure / 5.5.1：

Comparing Sets of Terms / 5.5.2：

Example of Collaboration / 5.5.3：

Conclusions / 5.6：

Micro-economic Control of Distributed Intelligent Personal Assistants / 6：

Stable Strategies / 6.1：

Network of Intelligent Personal Assistants / 6.2：

Definition of the Automatic PA / 6.2.1：

Further Specifications / 6.2.2：

The Intelligent Automatic PA / 6.2.3：

Negotiating and Optimizing Agents / 6.2.4：

An Example / 6.2.5：

Finding a Stable Strategy / 6.3：

The Discrete Event Simulator / 6.3.1：

A Stable Strategy / 6.3.2：

Conclusions and Future Work / 6.4：

Future Directions / 7.1：

Ecosystems of Networked Businesses / 7.2.1：

Exchange in Natural Ecosystems / 7.2.2：

Appendices / 7.3：

The EEII Project / A：

Statistical Analysis / B：

Statistical Hypothesis Testing / B.1：

Tests for Showing That Two Samples Come from the Same Distribution / B.2：

Methodology: Evolutionary Algorithms / C：

References

Index

Introduction / 1：

Background to the Research / 1.1：

Approach / 1.2：

41.

電子ブック

ＥＢ

41. Cognitive Engineering

Amit Konar, L. C. Jain, Lakhmi C. Jain

出版情報:	Springer eBooks Computer Science , Springer London, 2005
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Foreword

Preface

Acknowledgments

The Psychological Basis of Cognitive Modeling / Chapter 1：

Introduction / 1.1：

Cognitive Models of Pattern Recognition / 1.2：

Template-Matching Theory / 1.2.1：

Prototype-Matching Theory / 1.2.2：

Feature-Based Approach for Pattern Recognition / 1.2.3：

The Computational Approach / 1.2.4：

Cognitive Models of Memory / 1.3：

Atkinson-Shiffrin's Model / 1.3.1：

Debates on Atkinson-Shiffrin's Model / 1.3.2：

Tulving's Model / 1.3.3：

The Parallel Distributed Processing Approach / 1.3.4：

Mental Imagery / 1.4：

Mental Representation of Imagery / 1.4.1：

Rotation of Mental Imagery / 1.4.2：

Imagery and Size / 1.4.3：

Imagery and Shape / 1.4.4：

Part-Whole Relationship in Mental Imagery / 1.4.5：

Ambiguity in Mental Imagery / 1.4.6：

Neurophysiological Similarity between Imagery and Perception / 1.4.7：

Cognitive Maps of Mental Imagery / 1.4.8：

Understanding a Problem / 1.5：

Steps in Understanding a Problem / 1.5.1：

A Cybernetic View of Cognition / 1.6：

The States of Cognition / 1.6.1：

Computational Modeling of Cognitive Systems / 1.7：

Petri Nets: A Brief Review / 1.8：

Extension of Petri Net Models for Distributed Modeling of Cognition / 1.9：

Scope of the Book / 1.10：

Summary / 1.11：

Exercises

References

Parallel and Distributed Logic Programming / Chapter 2：

Formal Definitions / 2.1：

Preliminary Definitions / 2.2.1：

Properties of the Substitution Set / 2.2.2：

SLD Resolution / 2.2.3：

Concurrency in Resolution / 2.3：

Types of Concurrent Resolution / 2.3.1：

Petri Net Model for Concurrent Resolution / 2.4：

Extended Petri Net / 2.4.1：

Algorithm for Concurrent Resolution / 2.4.2：

Performance Analysis of Petri Net-Based Models / 2.5：

The Speed-up / 2.5.1：

The Resource Utilization Rate / 2.5.2：

Resource Unlimited Speed-up and Utilization Rate / 2.5.3：

Conclusions / 2.6：

Distributed Reasoning by Fuzzy Petri Nets: A Review / Chapter 3：

Fuzzy Logic and Approximate Reasoning / 3.1：

Structured Models of Approximate Reasoning / 3.2：

Looney's Model / 3.3：

The Model Proposed by Chen et al / 3.4：

Konar and Mandal's Model / 3.5：

Yu's Model / 3.6：

Chen's Model for Backward Reasoning / 3.7：

Bugarin and Barro's Model / 3.8：

Pedrycz and Gomide's Learning Model / 3.9：

Construction of Reduction Rules Using FPN / 3.10：

Scope of Extension of Fuzzy Reasoning on Petri Nets / 3.11：

Belief Propagation and Belief Revision Models in Fuzzy Petri Nets / 3.12：

Imprecision Management in an Acyclic FPN / 4.1：

Formal Definitions and the Proposed Model / 4.2.1：

Proposed Model for Belief Propagation / 4.2.2：

Proposed Algorithm for Belief Propagation / 4.2.3：

Properties of FPN and Belief Propagation Scheme / 4.2.4：

Imprecision and Inconsistency Management in a Cyclic FPN / 4.3：

Proposed Model for Belief Revision / 4.3.1：

Stability Analysis of the Belief Revision Model / 4.3.2：

Detection and Elimination of Limit Cycles / 4.3.3：

Nonmonotonic Reasoning in an FPN / 4.3.4：

Building Expert Systems Using Fuzzy Petri Nets / 4.4：

The Database / 5.1：

The Data-tree / 5.2.1：

The Knowledge Base / 5.3：

The Inference Engine / 5.4：

Searching Antecedent Parts of PR in the Data-tree / 5.4.1：

Formation of the FPN / 5.4.2：

Decision Making and Explanation Tracing / 5.4.3：

A Case Study / 5.5：

Performance Evaluation / 5.6：

Time-Complexisty for the Default-Data-Tree-Formation Procedure / 5.6.1：

Time-Complexity for the Procedure Suspect-Identification / 5.6.2：

Time-Complexity for the Procedure Variable-Instantiation-of-PRs / 5.6.3：

Time-Complexity for the Procedure Create-tree / 5.6.4：

Time-Complexity for the Procedure Search-on-Data-Tree / 5.6.5：

Time-Complexity for the Procedure FPN-Formation / 5.6.6：

Time-Complexity for the Belief-Revision and Limit-Cycle-Detection Procedure / 5.6.7：

Time-Complexity Analysis for the Procedure Limit-Cycle-Elimination / 5.6.8：

Time-Complexity for the Procedure Nonmonotonic Reasoning / 5.6.9：

Time-Complexity for the Procedure Decision-Making and Explanation Tracing / 5.6.10：

Time-Complexity of the Overall Expert System / 5.6.11：

Distributed Learning Using Fuzzy Cognitive Maps / 5.7：

Axelord's Cognitive Maps / 6.1：

Kosko's Model / 6.3：

Kosko's Extended Model / 6.4：

Adaptive FCMs / 6.5：

Zhang, Chen, and Bezdek's Model / 6.6：

Pal and Konar's FCM Model / 6.7：

Unsupervised Learning by Fuzzy Petri Nets / 6.8：

The Proposed Model for Cognitive Learning / 7.1：

Encoding of Weights / 7.2.1：

The Recall Model / 7.2.2：

State-Space Formulation / 7.3：

State-Space Model for Belief Updating / 7.3.1：

State-Space Model for FTT Updating of Transitions / 7.3.2：

State-Space Model for Weights / 7.3.3：

Stability Analysis of the Cognitive Model / 7.4：

Computer Simulation / 7.5：

Implication of the Results / 7.6：

Knowledge Refinement by Hebbian Learning / 7.7：

The Encoding Model / 7.7.1：

The Recall/Reasoning Model / 7.7.2：

Case Study by Computer Simulation / 7.7.3：

Supervised Learning by a Fuzzy Petri Net / 7.7.4：

Proposed Model of Fuzzy Petri Nets / 8.1：

Algorithm for Training / 8.2.1：

Analysis of Convergence / 8.4：

Application in Fuzzy Pattern Recognition / 8.5：

Distributed Modeling of Abduction, Reciprocity, and Duality by Fuzzy Petri Nets / 8.6：

State-Space Formulation of the Proposed FPN Model / 9.1：

The Behavioral Model of FPN / 9.3.1：

State-Space Formulation of the Model / 9.3.2：

Special Cases of the Model / 9.3.3：

Stability Analysis / 9.4：

Forward Reasoning in FPNs / 9.5：

Abductive Reasoning in FPN / 9.6：

Bi-directional Reasoning in an FPN / 9.7：

Fuzzy Modus Tollens and Duality / 9.8：

Human Mood Detection and Control: A Cybernetic Approach / 9.9：

Filtering, Segmentation and Localization of Facial Components / 10.1：

Segmentation of the Mouth Region / 10.2.1：

Segmentation of the Eye Region / 10.2.2：

Segmentation of Eyebrow Constriction / 10.2.3：

Determination of Facial Attributes / 10.3：

Determination of the Mouth-Opening / 10.3.1：

Determination of the Eye-Opening / 10.3.2：

Determination of the Length of Eyebrow-Constriction / 10.3.3：

Fuzzy Relational Model for Mood Detection / 10.4：

Fuzzification of Facial Attributes / 10.4.1：

The Fuzzy Relational Model for Mood Detection / 10.4.2：

Validation of System Performance / 10.5：

A Basic Scheme of Human Mood Control / 10.6：

A Simple Model of Human Mood Transition Dynamics / 10.7：

The Model / 10.7.1：

Properties of the Model / 10.7.2：

The Proportional Model of Human Mood Control / 10.8：

Mamdani's Model for Mood Control / 10.9：

Ranking the Music, Audio, and Video Clips / 10.10：

Experimental Results / 10.11：

Distributed Planning and Multi-agent Coordination of Robots / 10.12：

Single-Agent Planning / 11.1：

Multi-Agent Planning / 11.3：

Task Sharing and Distribution in Multi-agent Planning / 11.3.1：

Cooperation with/without Communication / 11.3.2：

Homogeneous and Heterogeneous Distributed Planning / 11.3.3：

Vision-based Transportation of Blocks by Two Robots / 11.4：

Timing Analysis of the Transportation Problem / 11.5：

Analysis with Two agents / 11.6.1：

Analysis with /-agents / 11.6.2：

Index / 11.7：

Foreword

Preface

Acknowledgments

42.

電子ブック

ＥＢ

42. Cognitive Engineering

Amit Konar, L. C. Jain, Lakhmi C. Jain

出版情報:	SpringerLink Books - AutoHoldings , Springer London, 2005
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目次情報: 続きを見る

Foreword

Preface

Acknowledgments

The Psychological Basis of Cognitive Modeling / Chapter 1：

Introduction / 1.1：

Cognitive Models of Pattern Recognition / 1.2：

Template-Matching Theory / 1.2.1：

Prototype-Matching Theory / 1.2.2：

Feature-Based Approach for Pattern Recognition / 1.2.3：

The Computational Approach / 1.2.4：

Cognitive Models of Memory / 1.3：

Atkinson-Shiffrin's Model / 1.3.1：

Debates on Atkinson-Shiffrin's Model / 1.3.2：

Tulving's Model / 1.3.3：

The Parallel Distributed Processing Approach / 1.3.4：

Mental Imagery / 1.4：

Mental Representation of Imagery / 1.4.1：

Rotation of Mental Imagery / 1.4.2：

Imagery and Size / 1.4.3：

Imagery and Shape / 1.4.4：

Part-Whole Relationship in Mental Imagery / 1.4.5：

Ambiguity in Mental Imagery / 1.4.6：

Neurophysiological Similarity between Imagery and Perception / 1.4.7：

Cognitive Maps of Mental Imagery / 1.4.8：

Understanding a Problem / 1.5：

Steps in Understanding a Problem / 1.5.1：

A Cybernetic View of Cognition / 1.6：

The States of Cognition / 1.6.1：

Computational Modeling of Cognitive Systems / 1.7：

Petri Nets: A Brief Review / 1.8：

Extension of Petri Net Models for Distributed Modeling of Cognition / 1.9：

Scope of the Book / 1.10：

Summary / 1.11：

Exercises

References

Parallel and Distributed Logic Programming / Chapter 2：

Formal Definitions / 2.1：

Preliminary Definitions / 2.2.1：

Properties of the Substitution Set / 2.2.2：

SLD Resolution / 2.2.3：

Concurrency in Resolution / 2.3：

Types of Concurrent Resolution / 2.3.1：

Petri Net Model for Concurrent Resolution / 2.4：

Extended Petri Net / 2.4.1：

Algorithm for Concurrent Resolution / 2.4.2：

Performance Analysis of Petri Net-Based Models / 2.5：

The Speed-up / 2.5.1：

The Resource Utilization Rate / 2.5.2：

Resource Unlimited Speed-up and Utilization Rate / 2.5.3：

Conclusions / 2.6：

Distributed Reasoning by Fuzzy Petri Nets: A Review / Chapter 3：

Fuzzy Logic and Approximate Reasoning / 3.1：

Structured Models of Approximate Reasoning / 3.2：

Looney's Model / 3.3：

The Model Proposed by Chen et al / 3.4：

Konar and Mandal's Model / 3.5：

Yu's Model / 3.6：

Chen's Model for Backward Reasoning / 3.7：

Bugarin and Barro's Model / 3.8：

Pedrycz and Gomide's Learning Model / 3.9：

Construction of Reduction Rules Using FPN / 3.10：

Scope of Extension of Fuzzy Reasoning on Petri Nets / 3.11：

Belief Propagation and Belief Revision Models in Fuzzy Petri Nets / 3.12：

Imprecision Management in an Acyclic FPN / 4.1：

Formal Definitions and the Proposed Model / 4.2.1：

Proposed Model for Belief Propagation / 4.2.2：

Proposed Algorithm for Belief Propagation / 4.2.3：

Properties of FPN and Belief Propagation Scheme / 4.2.4：

Imprecision and Inconsistency Management in a Cyclic FPN / 4.3：

Proposed Model for Belief Revision / 4.3.1：

Stability Analysis of the Belief Revision Model / 4.3.2：

Detection and Elimination of Limit Cycles / 4.3.3：

Nonmonotonic Reasoning in an FPN / 4.3.4：

Building Expert Systems Using Fuzzy Petri Nets / 4.4：

The Database / 5.1：

The Data-tree / 5.2.1：

The Knowledge Base / 5.3：

The Inference Engine / 5.4：

Searching Antecedent Parts of PR in the Data-tree / 5.4.1：

Formation of the FPN / 5.4.2：

Decision Making and Explanation Tracing / 5.4.3：

A Case Study / 5.5：

Performance Evaluation / 5.6：

Time-Complexisty for the Default-Data-Tree-Formation Procedure / 5.6.1：

Time-Complexity for the Procedure Suspect-Identification / 5.6.2：

Time-Complexity for the Procedure Variable-Instantiation-of-PRs / 5.6.3：

Time-Complexity for the Procedure Create-tree / 5.6.4：

Time-Complexity for the Procedure Search-on-Data-Tree / 5.6.5：

Time-Complexity for the Procedure FPN-Formation / 5.6.6：

Time-Complexity for the Belief-Revision and Limit-Cycle-Detection Procedure / 5.6.7：

Time-Complexity Analysis for the Procedure Limit-Cycle-Elimination / 5.6.8：

Time-Complexity for the Procedure Nonmonotonic Reasoning / 5.6.9：

Time-Complexity for the Procedure Decision-Making and Explanation Tracing / 5.6.10：

Time-Complexity of the Overall Expert System / 5.6.11：

Distributed Learning Using Fuzzy Cognitive Maps / 5.7：

Axelord's Cognitive Maps / 6.1：

Kosko's Model / 6.3：

Kosko's Extended Model / 6.4：

Adaptive FCMs / 6.5：

Zhang, Chen, and Bezdek's Model / 6.6：

Pal and Konar's FCM Model / 6.7：

Unsupervised Learning by Fuzzy Petri Nets / 6.8：

The Proposed Model for Cognitive Learning / 7.1：

Encoding of Weights / 7.2.1：

The Recall Model / 7.2.2：

State-Space Formulation / 7.3：

State-Space Model for Belief Updating / 7.3.1：

State-Space Model for FTT Updating of Transitions / 7.3.2：

State-Space Model for Weights / 7.3.3：

Stability Analysis of the Cognitive Model / 7.4：

Computer Simulation / 7.5：

Implication of the Results / 7.6：

Knowledge Refinement by Hebbian Learning / 7.7：

The Encoding Model / 7.7.1：

The Recall/Reasoning Model / 7.7.2：

Case Study by Computer Simulation / 7.7.3：

Supervised Learning by a Fuzzy Petri Net / 7.7.4：

Proposed Model of Fuzzy Petri Nets / 8.1：

Algorithm for Training / 8.2.1：

Analysis of Convergence / 8.4：

Application in Fuzzy Pattern Recognition / 8.5：

Distributed Modeling of Abduction, Reciprocity, and Duality by Fuzzy Petri Nets / 8.6：

State-Space Formulation of the Proposed FPN Model / 9.1：

The Behavioral Model of FPN / 9.3.1：

State-Space Formulation of the Model / 9.3.2：

Special Cases of the Model / 9.3.3：

Stability Analysis / 9.4：

Forward Reasoning in FPNs / 9.5：

Abductive Reasoning in FPN / 9.6：

Bi-directional Reasoning in an FPN / 9.7：

Fuzzy Modus Tollens and Duality / 9.8：

Human Mood Detection and Control: A Cybernetic Approach / 9.9：

Filtering, Segmentation and Localization of Facial Components / 10.1：

Segmentation of the Mouth Region / 10.2.1：

Segmentation of the Eye Region / 10.2.2：

Segmentation of Eyebrow Constriction / 10.2.3：

Determination of Facial Attributes / 10.3：

Determination of the Mouth-Opening / 10.3.1：

Determination of the Eye-Opening / 10.3.2：

Determination of the Length of Eyebrow-Constriction / 10.3.3：

Fuzzy Relational Model for Mood Detection / 10.4：

Fuzzification of Facial Attributes / 10.4.1：

The Fuzzy Relational Model for Mood Detection / 10.4.2：

Validation of System Performance / 10.5：

A Basic Scheme of Human Mood Control / 10.6：

A Simple Model of Human Mood Transition Dynamics / 10.7：

The Model / 10.7.1：

Properties of the Model / 10.7.2：

The Proportional Model of Human Mood Control / 10.8：

Mamdani's Model for Mood Control / 10.9：

Ranking the Music, Audio, and Video Clips / 10.10：

Experimental Results / 10.11：

Distributed Planning and Multi-agent Coordination of Robots / 10.12：

Single-Agent Planning / 11.1：

Multi-Agent Planning / 11.3：

Task Sharing and Distribution in Multi-agent Planning / 11.3.1：

Cooperation with/without Communication / 11.3.2：

Homogeneous and Heterogeneous Distributed Planning / 11.3.3：

Vision-based Transportation of Blocks by Two Robots / 11.4：

Timing Analysis of the Transportation Problem / 11.5：

Analysis with Two agents / 11.6.1：

Analysis with /-agents / 11.6.2：

Index / 11.7：

Foreword

Preface

Acknowledgments

43.

電子ブック

ＥＢ

43. Simulating the Mind

Dietmar; Fodor, Georg; Zucker, Gerhard Dietrich, Dietmar Dietrich, Georg Fodor

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Theory / Part I：

The Vision / 1：

Basics / 2：

Introduction to Automation / 2.1：

Introduction to Psychoanalysis / 2.2：

Psychoanalysis, a Natural Science? / 2.3：

Neuropsychoanalysis / 2.4：

Realizing Psychic Functions in a Machine / 2.5：

Automation as the Challenge for Psychoanalysis / 2.6：

Two Different Sciences - two Different Languages / 2.7：

Model / 3：

Modeling a Decision Unit for Autonomous Agents / 3.1：

Perception in Automation / 3.2：

Towards the new ARS-PA Model / 3.3：

The New Model and its Description: Top-Down-Design / 3.4：

Implementation and Application / 4：

Differentiation between Modeling and Implementation / 4.1：

The Bubble-World / 4.2：

Applying the Model / 4.3：

Possible Future Benefits for the Humanities / 4.4：

References

Proceedings of Emulating the Mind (ENF 2007) / Part II：

Session 1

A Brief Overview of Artificial Intelligence Focusing on Computational Models of Emotions / 1.1：

Considering a Technical Realization of a Neuropsychoanalytical Model of the Mind - A Theoretical Framework / 1.2：

What is the "Mind"? A Neuro-Psychoanalytical Approach / 1.3：

Discussion Chaired by Authors / 1.4：

Session 2

Machines in the Ghost

Simulating the Primal Affective Mentalities of the Mammalian Brain: A Fugue on the Emotional Feelings of Mental Life and Implications for AI-Robotics

Session 3

Cognitive and Affective Automation: Machines Using the Psychoanalytic Model of the Human Mind

Issues at the Interface of Artificial Intelligence and Psychoanalysis: Emotion, Consciousness, Transference

Session 4

The Prometheus Phantasy - Functions of the Human Psyche for Technical Systems

Return of the Zombie - Neuropsychoanalysis, Consciousness, and the Engineering of Psychic Functions

Discussion Sessions / 5：

Psychoanalysis and Computer Engineering / 5.1：

The Mammal in the Machine / 5.2：

The Remembering Body / 5.3：

Emotions, Drives and Desire (Silicone in Love) / 5.4：

Getting A Grasp / 5.5：

Free Will / 5.6：

Responses to the ENF 2007 / Part III：

Introductory Words

Collected Papers

A Computational Model of Affects

The Physics of Thoughts

A Functional View on "Cognitive" Perceptual Systems Based on Functions and Principles of the Human Mind

Four Laws of Machine Psychodynamics

Artificial Group Mind, a Psychoanalytically Founded Thought Experiment

Artificial Group Psychodynamics: Emergence of the Collective

A Primer of Psychoanalysis for Alan Turing

Alexander R. Luria and the Theory of Functional Systems / 2.8：

A Mind for Resolving the Interior-Exterior Distinctions / 2.9：

The Vision, Revisited / 2.10：

Explanations for Engineers and Psychoanalysts / Part IV：

Abbreviations

Index

Theory / Part I：

The Vision / 1：

Basics / 2：

44.

電子ブック

ＥＢ

44. Simulating the Mind

Dietmar; Fodor, Georg; Zucker, Gerhard Dietrich, Dietmar Dietrich, Georg Fodor, Dietmar Bruckner, Gerhard Zucker

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Theory / Part I：

The Vision / 1：

Basics / 2：

Introduction to Automation / 2.1：

Introduction to Psychoanalysis / 2.2：

Psychoanalysis, a Natural Science? / 2.3：

Neuropsychoanalysis / 2.4：

Realizing Psychic Functions in a Machine / 2.5：

Automation as the Challenge for Psychoanalysis / 2.6：

Two Different Sciences - two Different Languages / 2.7：

Model / 3：

Modeling a Decision Unit for Autonomous Agents / 3.1：

Perception in Automation / 3.2：

Towards the new ARS-PA Model / 3.3：

The New Model and its Description: Top-Down-Design / 3.4：

Implementation and Application / 4：

Differentiation between Modeling and Implementation / 4.1：

The Bubble-World / 4.2：

Applying the Model / 4.3：

Possible Future Benefits for the Humanities / 4.4：

References

Proceedings of Emulating the Mind (ENF 2007) / Part II：

Session 1

A Brief Overview of Artificial Intelligence Focusing on Computational Models of Emotions / 1.1：

Considering a Technical Realization of a Neuropsychoanalytical Model of the Mind - A Theoretical Framework / 1.2：

What is the "Mind"? A Neuro-Psychoanalytical Approach / 1.3：

Discussion Chaired by Authors / 1.4：

Session 2

Machines in the Ghost

Simulating the Primal Affective Mentalities of the Mammalian Brain: A Fugue on the Emotional Feelings of Mental Life and Implications for AI-Robotics

Session 3

Cognitive and Affective Automation: Machines Using the Psychoanalytic Model of the Human Mind

Issues at the Interface of Artificial Intelligence and Psychoanalysis: Emotion, Consciousness, Transference

Session 4

The Prometheus Phantasy - Functions of the Human Psyche for Technical Systems

Return of the Zombie - Neuropsychoanalysis, Consciousness, and the Engineering of Psychic Functions

Discussion Sessions / 5：

Psychoanalysis and Computer Engineering / 5.1：

The Mammal in the Machine / 5.2：

The Remembering Body / 5.3：

Emotions, Drives and Desire (Silicone in Love) / 5.4：

Getting A Grasp / 5.5：

Free Will / 5.6：

Responses to the ENF 2007 / Part III：

Introductory Words

Collected Papers

A Computational Model of Affects

The Physics of Thoughts

A Functional View on "Cognitive" Perceptual Systems Based on Functions and Principles of the Human Mind

Four Laws of Machine Psychodynamics

Artificial Group Mind, a Psychoanalytically Founded Thought Experiment

Artificial Group Psychodynamics: Emergence of the Collective

A Primer of Psychoanalysis for Alan Turing

Alexander R. Luria and the Theory of Functional Systems / 2.8：

A Mind for Resolving the Interior-Exterior Distinctions / 2.9：

The Vision, Revisited / 2.10：

Explanations for Engineers and Psychoanalysts / Part IV：

Abbreviations

Index

Theory / Part I：

The Vision / 1：

Basics / 2：

45.

電子ブック

ＥＢ

45. OMDoc ? An Open Markup Format for Mathematical Documents [version 1.2]

Michael Kohlhase, Takeo Kanade

出版情報:	Springer eBooks Computer Science , Springer Berlin Heidelberg, 2006
子書誌情報:	loading…
所蔵情報:	loading…

目次情報: 続きを見る

Setting the Stage for Open Mathematical Documents / Part I：

Document Markup for the Web / 1：

Structure vest. Appearance in Markup / 1.1：

Markup for the World Wide Web / 1.2：

XML, the eXtensible Markup Language / 1.3：

Markup for Mathematical Knowledge / 2：

Mathematical Objects and Formulae / 2.1：

Mathematical Texts and Statements / 2.2：

Large-Scale Structure and Context in Mathematics / 2.3：

Open Mathematical Documents / 3：

A Brief History of the OMDoc Format / 3.1：

Three Levels of Markup / 3.2：

Situating the OMDoc Format / 3.3：

The Future: An Active Web of (Mathematical) Knowledge / 3.4：

An OMDoc Primer / Part II：

Textbooks and Articles / 4：

Minimal OMDoc Markup / 4.1：

Structure and Statements / 4.2：

Marking up the Formulae / 4.3：

Full Formalization / 4.4：

OpenMath Content Dictionaries / 5：

Structured and Parametrized Theories / 6：

A Development Graph for Elementary Algebra / 7：

Courseware and the Narrative/Content Distinction / 8：

A Knowledge-Centered View / 8.1：

A Narrative-Structured View / 8.2：

Choreographing Narrative and Content OMDoc / 8.3：

Summary / 8.4：

Communication Between Systems / 9：

The OMDoc Document Format / Part III：

OMDoc as a Modular Format / 10：

The OMDoc Namespaces / 10.1：

Common Attributes in OMDoc / 10.2：

Document Infrastructure / 11：

The Document Root / 11.1：

Metadata / 11.2：

Document Comments / 11.3：

Document Structure / 11.4：

Sharing Document Parts / 11.5：

The Dublin Core Elements (Module DC) / 12：

Roles in Dublin Core Elements / 12.2：

Managing Rights / 12.3：

Inheritance of Metadata / 12.4：

Mathematical Objects / 13：

OpenMath / 13.1：

Content MathML / 13.2：

Representing Types in Content-MathML and OpenMath / 13.3：

Semantics of Variables / 13.4：

Legacy Representation for Migration / 13.5：

Mathematical Text / 14：

Multilingual Mathematical Vernacular / 14.1：

Formal Mathematical Properties / 14.2：

Text Fragments and Their Rhetoric/Mathematical Roles / 14.3：

Phrase-Level Markup of Mathematical Vernacular / 14.4：

Technical Terms / 14.5：

Rich Text Structure / 14.6：

Mathematical Statements / 15：

Types of Statements in Mathematics / 15.1：

Theory-Constitutive Statements in OMDoc / 15.2：

The Unassuming Rest / 15.3：

Mathematical Examples in OMDoc / 15.4：

Inline Statements / 15.5：

Theories as Structured Contexts / 15.6：

Abstract Data Types / 16：

Representing Proofs / 17：

Proof Structure / 17.1：

Proof Step Justifications / 17.2：

Scoping and Context in a Proof / 17.3：

Formal Proofs as Mathematical Objects / 17.4：

Complex Theories / 18：

Inheritance via Translations / 18.1：

Postulated Theory Inclusions / 18.2：

Local/Required Theory Inclusions / 18.3：

Induced Assertions / 18.4：

Development Graphs / 18.5：

Notation and Presentation / 19：

Styling OMDoc Elements / 19.1：

A Restricted Style Language / 19.2：

Notation of Symbols / 19.3：

Presenting Bound Variables / 19.4：

Auxiliary Elements / 20：

Non-XML Data and Program Code in OMDoc / 20.1：

Applets and External Objects in OMDoc / 20.2：

Exercises / 21：

Document Models for OMDoc / 22：

XML Document Models / 22.1：

The OMDoc Document Model / 22.2：

OMDoc Sub-Languages / 22.3：

OMDoc Applications, Tools, and Projects / Part IV：

OMDoc Resources / 23：

The OMDoc Web Site, Wiki, and Mailing List / 23.1：

The OMDoc Distribution / 23.2：

The OMDoc Bug Tracker / 23.3：

An XML Catalog for OMDoc / 23.4：

External Resources / 23.5：

Validating OMDoc Documents / 24：

Validation with Document Type Definitions / 24.1：

Validation with RelaxNG Schemata / 24.2：

Validation with XML Schema / 24.3：

Transforming OMDoc / 25：

Extracting and Linking XSLT Templates / 25.1：

Interfaces for Systems / 25.2：

Presenting OMDoc to Humans / 25.3：

Applications and Projects / 26：

Introduction / 26.1：

QMath Parser / 26.2：

Sentido Integrated Environment / 26.3：

MBase / 26.4：

A Search Engine for Mathematical Formulae / 26.5：

Semantic Interrelation and Change Management / 26.6：

MathDox / 26.7：

ActiveMath / 26.8：

Authoring Tools for ActiveMath / 26.9：

SWiM - An OMDoc-Based Semantic Wiki / 26.10：

Induction Challenge Problems / 26.11：

Maya / 26.12：

Hets / 26.13：

CPoint / 26.14：

Stex: A Latex-Based Workflow for OMDoc / 26.15：

An Emacs Mode for Editing OMDoc Documents / 26.16：

Converting Mathematica Notebooks to OMDoc / 26.17：

Standardizing Context in System Interoperability / 26.18：

Proof Assistants in Scientific Editors / 26.19：

VeriFun / 26.20：

Appendix / Part V：

Changes to the Specification / A：

Changes from 1.1 to 1.2 / A.1：

Changes from 1.0 to 1.1 / A.2：

Quick-Reference / B：

Table of Attributes / C：

The RelaxNG Schema for OMDoc / D：

The Sub-language Drivers / D.1：

Common Attributes / D.2：

Module MOBJ: Mathematical Objects and Text / D.3：

Module MTXT: Mathematical Text / D.4：

Module DOC: Document Infrastructure / D.5：

Module DC: Dublin Core Metadata / D.6：

Module ST: Mathematical Statements / D.7：

Module ADT: Abstract Data Types / D.8：

Module PF: Proofs and Proof objects / D.9：

Module CTH: Complex Theories / D.10：

Module RT: Rich Text Structure / D.11：

Module EXT: Applets and Non-XML Data / D.12：

Module PRES: Adding Presentation Information / D.13：

Module QUIZ: Infrastructure for Assessments / D.14：

The RelaxNG Schemata for Mathematical Objects / E：

The RelaxNG Schema for OpenMath / E.1：

The RelaxNG Schema for MathML / E.2：

Bibliography

Index

Setting the Stage for Open Mathematical Documents / Part I：

Document Markup for the Web / 1：

Structure vest. Appearance in Markup / 1.1：

46.

電子ブック

ＥＢ

46. Embedded Robotics

Thomas Br?unl

出版情報:	Springer eBooks Computer Science , Springer Berlin Heidelberg, 2008
子書誌情報:	loading…
所蔵情報:	loading…

目次情報: 続きを見る

Embedded Systems / Part I：

Robots and Controllers / 1：

Mobile Robots / 1.1：

Embedded Controllers / 1.2：

Interfaces / 1.3：

Operating System / 1.4：

References / 1.5：

Central Processing Unit / 2：

Logic Gates / 2.1：

Function Units / 2.2：

Registers and Memory / 2.3：

Retro / 2.4：

Arithmetic Logic Unit / 2.5：

Control Unit / 2.6：

Sensors / 2.7：

Sensor Categories / 3.1：

Binary Sensor / 3.2：

Analog versus Digital Sensors / 3.3：

Shaft Encoder / 3.4：

A/D Converter / 3.5：

Position Sensitive Device / 3.6：

Compass / 3.7：

Gyroscope, Accelerometer, Inclinometer / 3.8：

Digital Camera / 3.9：

Actuators / 3.10：

DC Motors / 4.1：

H-Bridge / 4.2：

Pulse Width Modulation / 4.3：

Stepper Motors / 4.4：

Servos / 4.5：

Control / 4.6：

On-Off Control / 5.1：

PID Control / 5.2：

Velocity Control and Position Control / 5.3：

Multiple Motors - Driving Straight / 5.4：

V-Omega Interface / 5.5：

Multitasking / 5.6：

Cooperative Multitasking / 6.1：

Preemptive Multitasking / 6.2：

Synchronization / 6.3：

Scheduling / 6.4：

Interrupts and Timer-Activated Tasks / 6.5：

Wireless Communication / 6.6：

Communication Model / 7.1：

Messages / 7.2：

Fault-Tolerant Self-Configuration / 7.3：

User Interface and Remote Control / 7.4：

Sample Application Program / 7.5：

Mobile Robot Design / 7.6：

Driving Robots / 8：

Single Wheel Drive / 8.1：

Differential Drive / 8.2：

Tracked Robots / 8.3：

Synchro-Drive / 8.4：

Ackermann Steering / 8.5：

Drive Kinematics / 8.6：

Omni-Directional Robots / 8.7：

Mecanum Wheels / 9.1：

Omni-Directional Drive / 9.2：

Kinematics / 9.3：

Omni-Directional Robot Design / 9.4：

Driving Program / 9.5：

Balancing Robots / 9.6：

Simulation / 10.1：

Inverted Pendulum Robot / 10.2：

Double Inverted Pendulum / 10.3：

Walking Robots / 10.4：

Six-Legged Robot Design / 11.1：

Biped Robot Design / 11.2：

Sensors for Walking Robots / 11.3：

Static Balance / 11.4：

Dynamic Balance / 11.5：

Autonomous Planes / 11.6：

Application / 12.1：

Control System and Sensors / 12.2：

Flight Program / 12.3：

Autonomous Vessels and Underwater Vehicles / 12.4：

Dynamic Model / 13.1：

AUV Design Mako / 13.3：

AUV Design USAL / 13.4：

Robot Manipulators / 13.5：

Homogeneous Coordinates / 14.1：

Simulation and Programming / 14.2：

Simulation Systems / 14.4：

Mobile Robot Simulation / 15.1：

EyeSim Simulation System / 15.2：

Multiple Robot Simulation / 15.3：

EyeSim Application / 15.4：

EyeSim Environment and Parameter Files / 15.5：

SubSim Simulation System / 15.6：

Actuator and Sensor Models / 15.7：

SubSim Application / 15.8：

SubSim Environment and Parameter Files / 15.9：

Mobile Robot Applications / 15.10：

Localization and Navigation / 16：

Localization / 16.1：

Probabilistic Localization / 16.2：

Coordinate Systems / 16.3：

Environment Representation / 16.4：

Visibility Graph / 16.5：

Voronoi Diagram / 16.6：

Potential Field Method / 16.7：

Wandering Standpoint Algorithm / 16.8：

Bug Algorithm Family / 16.9：

Dijkstra's Algorithm / 16.10：

A* Algorithm / 16.11：

Maze Exploration / 16.12：

Micro Mouse Contest / 17.1：

Maze Exploration Algorithms / 17.2：

Simulated versus Real Maze Program / 17.3：

Map Generation / 17.4：

Mapping Algorithm / 18.1：

Data Representation / 18.2：

Boundary-Following Algorithm / 18.3：

Algorithm Execution / 18.4：

Simulation Experiments / 18.5：

Robot Experiments / 18.6：

Results / 18.7：

Real-Time Image Processing / 18.8：

Camera Interface / 19.1：

Auto-Brightness / 19.2：

Edge Detection / 19.3：

Motion Detection / 19.4：

Color Space / 19.5：

Color Object Detection / 19.6：

Image Segmentation / 19.7：

Image Coordinates versus World Coordinates / 19.8：

Robot Soccer / 19.9：

RoboCup and FIRA Competitions / 20.1：

Team Structure / 20.2：

Mechanics and Actuators / 20.3：

Sensing / 20.4：

Image Processing / 20.5：

Trajectory Planning / 20.6：

Neural Networks / 20.7：

Neural Network Principles / 21.1：

Feed-Forward Networks / 21.2：

Backpropagation / 21.3：

Neural Network Examples / 21.4：

Neural Controller / 21.5：

Genetic Algorithms / 21.6：

Genetic Algorithm Principles / 22.1：

Genetic Operators / 22.2：

Applications to Robot Control / 22.3：

Example Evolution / 22.4：

Implementation of Genetic Algorithms / 22.5：

Starman / 22.6：

Genetic Programming / 22.7：

Concepts and Applications / 23.1：

Lisp / 23.2：

Evolution / 23.3：

Tracking Problem / 23.5：

Evolution of Tracking Behavior / 23.6：

Behavior-Based Systems / 23.7：

Software Architecture / 24.1：

Behavior-Based Robotics / 24.2：

Behavior-Based Applications / 24.3：

Behavior Framework / 24.4：

Adaptive Controller / 24.5：

Neural Network Controller / 24.6：

Experiments / 24.8：

Evolution of Walking Gaits / 24.9：

Splines / 25.1：

Control Algorithm / 25.2：

Incorporating Feedback / 25.3：

Controller Evolution / 25.4：

Controller Assessment / 25.5：

Evolved Gaits / 25.6：

Automotive Systems / 25.7：

Autonomous Automobiles / 26.1：

Automobile Conversion for Autonomous Driving / 26.2：

Computer Vision for Driver-Assistance Systems / 26.3：

Image Processing Framework / 26.4：

Lane Detection / 26.5：

Vehicle Recognition and Tracking / 26.6：

Automatic Parking / 26.7：

Outlook / 26.8：

Appendices

Programming Tools / A：

RoBIOS Operating System / B：

Hardware Description Table / C：

Hardware Specification / D：

Laboratories / E：

Solutions / F：

Index

Embedded Systems / Part I：

Robots and Controllers / 1：

Mobile Robots / 1.1：

47.

電子ブック

ＥＢ

47. Embedded Robotics

Thomas Bräunl

出版情報:	SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2008
子書誌情報:	loading…
所蔵情報:	loading…

目次情報: 続きを見る

Embedded Systems / Part I：

Robots and Controllers / 1：

Mobile Robots / 1.1：

Embedded Controllers / 1.2：

Interfaces / 1.3：

Operating System / 1.4：

References / 1.5：

Central Processing Unit / 2：

Logic Gates / 2.1：

Function Units / 2.2：

Registers and Memory / 2.3：

Retro / 2.4：

Arithmetic Logic Unit / 2.5：

Control Unit / 2.6：

Sensors / 2.7：

Sensor Categories / 3.1：

Binary Sensor / 3.2：

Analog versus Digital Sensors / 3.3：

Shaft Encoder / 3.4：

A/D Converter / 3.5：

Position Sensitive Device / 3.6：

Compass / 3.7：

Gyroscope, Accelerometer, Inclinometer / 3.8：

Digital Camera / 3.9：

Actuators / 3.10：

DC Motors / 4.1：

H-Bridge / 4.2：

Pulse Width Modulation / 4.3：

Stepper Motors / 4.4：

Servos / 4.5：

Control / 4.6：

On-Off Control / 5.1：

PID Control / 5.2：

Velocity Control and Position Control / 5.3：

Multiple Motors - Driving Straight / 5.4：

V-Omega Interface / 5.5：

Multitasking / 5.6：

Cooperative Multitasking / 6.1：

Preemptive Multitasking / 6.2：

Synchronization / 6.3：

Scheduling / 6.4：

Interrupts and Timer-Activated Tasks / 6.5：

Wireless Communication / 6.6：

Communication Model / 7.1：

Messages / 7.2：

Fault-Tolerant Self-Configuration / 7.3：

User Interface and Remote Control / 7.4：

Sample Application Program / 7.5：

Mobile Robot Design / 7.6：

Driving Robots / 8：

Single Wheel Drive / 8.1：

Differential Drive / 8.2：

Tracked Robots / 8.3：

Synchro-Drive / 8.4：

Ackermann Steering / 8.5：

Drive Kinematics / 8.6：

Omni-Directional Robots / 8.7：

Mecanum Wheels / 9.1：

Omni-Directional Drive / 9.2：

Kinematics / 9.3：

Omni-Directional Robot Design / 9.4：

Driving Program / 9.5：

Balancing Robots / 9.6：

Simulation / 10.1：

Inverted Pendulum Robot / 10.2：

Double Inverted Pendulum / 10.3：

Walking Robots / 10.4：

Six-Legged Robot Design / 11.1：

Biped Robot Design / 11.2：

Sensors for Walking Robots / 11.3：

Static Balance / 11.4：

Dynamic Balance / 11.5：

Autonomous Planes / 11.6：

Application / 12.1：

Control System and Sensors / 12.2：

Flight Program / 12.3：

Autonomous Vessels and Underwater Vehicles / 12.4：

Dynamic Model / 13.1：

AUV Design Mako / 13.3：

AUV Design USAL / 13.4：

Robot Manipulators / 13.5：

Homogeneous Coordinates / 14.1：

Simulation and Programming / 14.2：

Simulation Systems / 14.4：

Mobile Robot Simulation / 15.1：

EyeSim Simulation System / 15.2：

Multiple Robot Simulation / 15.3：

EyeSim Application / 15.4：

EyeSim Environment and Parameter Files / 15.5：

SubSim Simulation System / 15.6：

Actuator and Sensor Models / 15.7：

SubSim Application / 15.8：

SubSim Environment and Parameter Files / 15.9：

Mobile Robot Applications / 15.10：

Localization and Navigation / 16：

Localization / 16.1：

Probabilistic Localization / 16.2：

Coordinate Systems / 16.3：

Environment Representation / 16.4：

Visibility Graph / 16.5：

Voronoi Diagram / 16.6：

Potential Field Method / 16.7：

Wandering Standpoint Algorithm / 16.8：

Bug Algorithm Family / 16.9：

Dijkstra's Algorithm / 16.10：

A* Algorithm / 16.11：

Maze Exploration / 16.12：

Micro Mouse Contest / 17.1：

Maze Exploration Algorithms / 17.2：

Simulated versus Real Maze Program / 17.3：

Map Generation / 17.4：

Mapping Algorithm / 18.1：

Data Representation / 18.2：

Boundary-Following Algorithm / 18.3：

Algorithm Execution / 18.4：

Simulation Experiments / 18.5：

Robot Experiments / 18.6：

Results / 18.7：

Real-Time Image Processing / 18.8：

Camera Interface / 19.1：

Auto-Brightness / 19.2：

Edge Detection / 19.3：

Motion Detection / 19.4：

Color Space / 19.5：

Color Object Detection / 19.6：

Image Segmentation / 19.7：

Image Coordinates versus World Coordinates / 19.8：

Robot Soccer / 19.9：

RoboCup and FIRA Competitions / 20.1：

Team Structure / 20.2：

Mechanics and Actuators / 20.3：

Sensing / 20.4：

Image Processing / 20.5：

Trajectory Planning / 20.6：

Neural Networks / 20.7：

Neural Network Principles / 21.1：

Feed-Forward Networks / 21.2：

Backpropagation / 21.3：

Neural Network Examples / 21.4：

Neural Controller / 21.5：

Genetic Algorithms / 21.6：

Genetic Algorithm Principles / 22.1：

Genetic Operators / 22.2：

Applications to Robot Control / 22.3：

Example Evolution / 22.4：

Implementation of Genetic Algorithms / 22.5：

Starman / 22.6：

Genetic Programming / 22.7：

Concepts and Applications / 23.1：

Lisp / 23.2：

Evolution / 23.3：

Tracking Problem / 23.5：

Evolution of Tracking Behavior / 23.6：

Behavior-Based Systems / 23.7：

Software Architecture / 24.1：

Behavior-Based Robotics / 24.2：

Behavior-Based Applications / 24.3：

Behavior Framework / 24.4：

Adaptive Controller / 24.5：

Neural Network Controller / 24.6：

Experiments / 24.8：

Evolution of Walking Gaits / 24.9：

Splines / 25.1：

Control Algorithm / 25.2：

Incorporating Feedback / 25.3：

Controller Evolution / 25.4：

Controller Assessment / 25.5：

Evolved Gaits / 25.6：

Automotive Systems / 25.7：

Autonomous Automobiles / 26.1：

Automobile Conversion for Autonomous Driving / 26.2：

Computer Vision for Driver-Assistance Systems / 26.3：

Image Processing Framework / 26.4：

Lane Detection / 26.5：

Vehicle Recognition and Tracking / 26.6：

Automatic Parking / 26.7：

Outlook / 26.8：

Appendices

Programming Tools / A：

RoBIOS Operating System / B：

Hardware Description Table / C：

Hardware Specification / D：

Laboratories / E：

Solutions / F：

Index

Embedded Systems / Part I：

Robots and Controllers / 1：

Mobile Robots / 1.1：

48.

電子ブック

ＥＢ

48. OMDoc – An Open Markup Format for Mathematical Documents [version 1.2]

Michael Kohlhase, Takeo Kanade, Josef Kittler

出版情報:	SpringerLink Books - AutoHoldings , Springer Berlin Heidelberg, 2006
子書誌情報:	loading…
所蔵情報:	loading…

目次情報: 続きを見る

Setting the Stage for Open Mathematical Documents / Part I：

Document Markup for the Web / 1：

Structure vest. Appearance in Markup / 1.1：

Markup for the World Wide Web / 1.2：

XML, the eXtensible Markup Language / 1.3：

Markup for Mathematical Knowledge / 2：

Mathematical Objects and Formulae / 2.1：

Mathematical Texts and Statements / 2.2：

Large-Scale Structure and Context in Mathematics / 2.3：

Open Mathematical Documents / 3：

A Brief History of the OMDoc Format / 3.1：

Three Levels of Markup / 3.2：

Situating the OMDoc Format / 3.3：

The Future: An Active Web of (Mathematical) Knowledge / 3.4：

An OMDoc Primer / Part II：

Textbooks and Articles / 4：

Minimal OMDoc Markup / 4.1：

Structure and Statements / 4.2：

Marking up the Formulae / 4.3：

Full Formalization / 4.4：

OpenMath Content Dictionaries / 5：

Structured and Parametrized Theories / 6：

A Development Graph for Elementary Algebra / 7：

Courseware and the Narrative/Content Distinction / 8：

A Knowledge-Centered View / 8.1：

A Narrative-Structured View / 8.2：

Choreographing Narrative and Content OMDoc / 8.3：

Summary / 8.4：

Communication Between Systems / 9：

The OMDoc Document Format / Part III：

OMDoc as a Modular Format / 10：

The OMDoc Namespaces / 10.1：

Common Attributes in OMDoc / 10.2：

Document Infrastructure / 11：

The Document Root / 11.1：

Metadata / 11.2：

Document Comments / 11.3：

Document Structure / 11.4：

Sharing Document Parts / 11.5：

The Dublin Core Elements (Module DC) / 12：

Roles in Dublin Core Elements / 12.2：

Managing Rights / 12.3：

Inheritance of Metadata / 12.4：

Mathematical Objects / 13：

OpenMath / 13.1：

Content MathML / 13.2：

Representing Types in Content-MathML and OpenMath / 13.3：

Semantics of Variables / 13.4：

Legacy Representation for Migration / 13.5：

Mathematical Text / 14：

Multilingual Mathematical Vernacular / 14.1：

Formal Mathematical Properties / 14.2：

Text Fragments and Their Rhetoric/Mathematical Roles / 14.3：

Phrase-Level Markup of Mathematical Vernacular / 14.4：

Technical Terms / 14.5：

Rich Text Structure / 14.6：

Mathematical Statements / 15：

Types of Statements in Mathematics / 15.1：

Theory-Constitutive Statements in OMDoc / 15.2：

The Unassuming Rest / 15.3：

Mathematical Examples in OMDoc / 15.4：

Inline Statements / 15.5：

Theories as Structured Contexts / 15.6：

Abstract Data Types / 16：

Representing Proofs / 17：

Proof Structure / 17.1：

Proof Step Justifications / 17.2：

Scoping and Context in a Proof / 17.3：

Formal Proofs as Mathematical Objects / 17.4：

Complex Theories / 18：

Inheritance via Translations / 18.1：

Postulated Theory Inclusions / 18.2：

Local/Required Theory Inclusions / 18.3：

Induced Assertions / 18.4：

Development Graphs / 18.5：

Notation and Presentation / 19：

Styling OMDoc Elements / 19.1：

A Restricted Style Language / 19.2：

Notation of Symbols / 19.3：

Presenting Bound Variables / 19.4：

Auxiliary Elements / 20：

Non-XML Data and Program Code in OMDoc / 20.1：

Applets and External Objects in OMDoc / 20.2：

Exercises / 21：

Document Models for OMDoc / 22：

XML Document Models / 22.1：

The OMDoc Document Model / 22.2：

OMDoc Sub-Languages / 22.3：

OMDoc Applications, Tools, and Projects / Part IV：

OMDoc Resources / 23：

The OMDoc Web Site, Wiki, and Mailing List / 23.1：

The OMDoc Distribution / 23.2：

The OMDoc Bug Tracker / 23.3：

An XML Catalog for OMDoc / 23.4：

External Resources / 23.5：

Validating OMDoc Documents / 24：

Validation with Document Type Definitions / 24.1：

Validation with RelaxNG Schemata / 24.2：

Validation with XML Schema / 24.3：

Transforming OMDoc / 25：

Extracting and Linking XSLT Templates / 25.1：

Interfaces for Systems / 25.2：

Presenting OMDoc to Humans / 25.3：

Applications and Projects / 26：

Introduction / 26.1：

QMath Parser / 26.2：

Sentido Integrated Environment / 26.3：

MBase / 26.4：

A Search Engine for Mathematical Formulae / 26.5：

Semantic Interrelation and Change Management / 26.6：

MathDox / 26.7：

ActiveMath / 26.8：

Authoring Tools for ActiveMath / 26.9：

SWiM - An OMDoc-Based Semantic Wiki / 26.10：

Induction Challenge Problems / 26.11：

Maya / 26.12：

Hets / 26.13：

CPoint / 26.14：

Stex: A Latex-Based Workflow for OMDoc / 26.15：

An Emacs Mode for Editing OMDoc Documents / 26.16：

Converting Mathematica Notebooks to OMDoc / 26.17：

Standardizing Context in System Interoperability / 26.18：

Proof Assistants in Scientific Editors / 26.19：

VeriFun / 26.20：

Appendix / Part V：

Changes to the Specification / A：

Changes from 1.1 to 1.2 / A.1：

Changes from 1.0 to 1.1 / A.2：

Quick-Reference / B：

Table of Attributes / C：

The RelaxNG Schema for OMDoc / D：

The Sub-language Drivers / D.1：

Common Attributes / D.2：

Module MOBJ: Mathematical Objects and Text / D.3：

Module MTXT: Mathematical Text / D.4：

Module DOC: Document Infrastructure / D.5：

Module DC: Dublin Core Metadata / D.6：

Module ST: Mathematical Statements / D.7：

Module ADT: Abstract Data Types / D.8：

Module PF: Proofs and Proof objects / D.9：

Module CTH: Complex Theories / D.10：

Module RT: Rich Text Structure / D.11：

Module EXT: Applets and Non-XML Data / D.12：

Module PRES: Adding Presentation Information / D.13：

Module QUIZ: Infrastructure for Assessments / D.14：

The RelaxNG Schemata for Mathematical Objects / E：

The RelaxNG Schema for OpenMath / E.1：

The RelaxNG Schema for MathML / E.2：

Bibliography

Index

Setting the Stage for Open Mathematical Documents / Part I：

Document Markup for the Web / 1：

Structure vest. Appearance in Markup / 1.1：

49.

電子ブック

ＥＢ

49. The Agent Modeling Language - AML

Radovan Cervenka, Stefan Brantschen, Ivan Trencansky, Marius Walliser

出版情報:	Springer eBooks Computer Science , Birkh?user Basel, 2007
子書誌情報:	loading…
所蔵情報:	loading…

目次情報: 続きを見る

Introduction / 1：

Overview / 1.1：

Goals of this Work / 1.2：

Outline of the Book / 1.3：

Background Information / Part I：

Survey on Agent-Oriented Modeling Languages / 2：

Gaia / 2.1：

AUML / 2.2：

MESSAGE / 2.3：

Tropos / 2.4：

MAS-ML / 2.5：

AOR / 2.6：

Summary of Today's MAS Modeling Languages / 2.7：

Requirements on a MAS Modeling Language / 3：

Solution Summary / Part II：

The AML Approach / 4：

The Purpose of AML / 4.1：

The Scope of AML / 4.2：

The Development of AML / 4.3：

AML Sources / 4.4：

The Language Architecture / 4.5：

Concepts of AML / 5：

Multi-Agent System / 5.1：

MAS Semi-entities and Entities / 5.2：

Structural Aspects / 5.3：

Social Aspects / 5.4：

MAS Deployment and Mobility / 5.5：

Behaviors / 5.6：

Mental Aspects / 5.7：

Ontologies / 5.8：

AML Modeling Mechanisms / 6：

Generic Modeling Mechanisms / 6.1：

Modeling Entity Types / 6.2：

Modeling Social Aspects / 6.3：

Modeling MAS Deployment and Mobility / 6.4：

Modeling Capabilities and Behavior Decomposition / 6.5：

Modeling Interactions / 6.6：

Modeling Mental Aspects / 6.7：

Modeling Ontologies / 6.8：

Modeling Contexts / 6.9：

Related Work / 7：

CASE Tool Support / 7.1：

Methodological Support / 7.2：

Practical Apphcation of AML / 7.3：

Standardization Activities / 7.4：

AML Specification / Part III：

Extensions to Standard UML Notation / 8：

Stereotyped Classifier / 8.1：

ConnectableElement with a Stereotyped Type / 8.2：

Connector with a Stereotyped Type / 8.3：

Lifeline with a Stereotyped Type / 8.4：

Composed Lifelines in Communication Diagrams / 8.5：

ObjectNode with a Stereotyped Type / 8.6：

Bi-directional Dependencies / 8.7：

Internal Structure of ConnectableElements / 8.8：

Organization of the AML Specification / 9：

Overall AML Package Structure / 9.1：

Specification Structure / 9.2：

Architecture / 10：

Entities / 10.1：

Agents / 10.2：

Resources / 10.3：

Environments / 10.4：

MAS Deployment / 10.5：

Basic Behaviors / 11：

Behavior Decomposition / 11.2：

Communicative Interactions / 11.3：

Services / 11.4：

Observations and Effecting Interactions / 11.5：

Mobility / 11.6：

Mental / 12：

Mental States / 12.1：

Beliefs / 12.2：

Goals / 12.3：

Plans / 12.4：

Mental Relationships / 12.5：

Basic Ontologies / 13：

Model Management / 14：

Contexts / 14.1：

UML Extension for AM L / 15：

Extended Actor / 15.1：

Extended BehavioralFeature / 15.2：

Extended Behavior / 15.3：

Diagrams / 16：

Diagram Frames / 16.1：

Diagram Types / 16.2：

Extension of OCL / 17：

New Operators / 17.1：

Final Remarks / Part IV：

Conclusions / 18：

Context of the Work / 18.1：

Solution / 18.2：

Challenges / 18.3：

Results / 18.4：

Summary of Original Contribution / 18.5：

Further Work / 19：

Improvements of AML / 19.1：

Broader Application of AML / 19.2：

Assurance of Future Work / 19.3：

Bibliography

List of Acronyms

Index

Introduction / 1：

Overview / 1.1：

Goals of this Work / 1.2：

50.

電子ブック

ＥＢ

50. The Agent Modeling Language - AML

Radovan Cervenka, Stefan Brantschen, Ivan Trencansky, Marius Walliser, Monique Calisti

出版情報:	SpringerLink Books - AutoHoldings , Birkhäuser Basel, 2007
子書誌情報:	loading…
所蔵情報:	loading…

目次情報: 続きを見る

Introduction / 1：

Overview / 1.1：

Goals of this Work / 1.2：

Outline of the Book / 1.3：

Background Information / Part I：

Survey on Agent-Oriented Modeling Languages / 2：

Gaia / 2.1：

AUML / 2.2：

MESSAGE / 2.3：

Tropos / 2.4：

MAS-ML / 2.5：

AOR / 2.6：

Summary of Today's MAS Modeling Languages / 2.7：

Requirements on a MAS Modeling Language / 3：

Solution Summary / Part II：

The AML Approach / 4：

The Purpose of AML / 4.1：

The Scope of AML / 4.2：

The Development of AML / 4.3：

AML Sources / 4.4：

The Language Architecture / 4.5：

Concepts of AML / 5：

Multi-Agent System / 5.1：

MAS Semi-entities and Entities / 5.2：

Structural Aspects / 5.3：

Social Aspects / 5.4：

MAS Deployment and Mobility / 5.5：

Behaviors / 5.6：

Mental Aspects / 5.7：

Ontologies / 5.8：

AML Modeling Mechanisms / 6：

Generic Modeling Mechanisms / 6.1：

Modeling Entity Types / 6.2：

Modeling Social Aspects / 6.3：

Modeling MAS Deployment and Mobility / 6.4：

Modeling Capabilities and Behavior Decomposition / 6.5：

Modeling Interactions / 6.6：

Modeling Mental Aspects / 6.7：

Modeling Ontologies / 6.8：

Modeling Contexts / 6.9：

Related Work / 7：

CASE Tool Support / 7.1：

Methodological Support / 7.2：

Practical Apphcation of AML / 7.3：

Standardization Activities / 7.4：

AML Specification / Part III：

Extensions to Standard UML Notation / 8：

Stereotyped Classifier / 8.1：

ConnectableElement with a Stereotyped Type / 8.2：

Connector with a Stereotyped Type / 8.3：

Lifeline with a Stereotyped Type / 8.4：

Composed Lifelines in Communication Diagrams / 8.5：

ObjectNode with a Stereotyped Type / 8.6：

Bi-directional Dependencies / 8.7：

Internal Structure of ConnectableElements / 8.8：

Organization of the AML Specification / 9：

Overall AML Package Structure / 9.1：

Specification Structure / 9.2：

Architecture / 10：

Entities / 10.1：

Agents / 10.2：

Resources / 10.3：

Environments / 10.4：

MAS Deployment / 10.5：

Basic Behaviors / 11：

Behavior Decomposition / 11.2：

Communicative Interactions / 11.3：

Services / 11.4：

Observations and Effecting Interactions / 11.5：

Mobility / 11.6：

Mental / 12：

Mental States / 12.1：

Beliefs / 12.2：

Goals / 12.3：

Plans / 12.4：

Mental Relationships / 12.5：

Basic Ontologies / 13：

Model Management / 14：

Contexts / 14.1：

UML Extension for AM L / 15：

Extended Actor / 15.1：

Extended BehavioralFeature / 15.2：

Extended Behavior / 15.3：

Diagrams / 16：

Diagram Frames / 16.1：

Diagram Types / 16.2：

Extension of OCL / 17：

New Operators / 17.1：

Final Remarks / Part IV：

Conclusions / 18：

Context of the Work / 18.1：

Solution / 18.2：

Challenges / 18.3：

Results / 18.4：

Summary of Original Contribution / 18.5：

Further Work / 19：

Improvements of AML / 19.1：

Broader Application of AML / 19.2：

Assurance of Future Work / 19.3：

Bibliography

List of Acronyms

Index

Introduction / 1：

Overview / 1.1：

Goals of this Work / 1.2：

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