close
1.

図書
図書
1. Rough fuzzy hybridization : a new trend in decision-making
S.K. Pal, A. Skowron, editors
出版情報: Singapore ; New York : Springer Verlag, 1999  xiv, 454 p. ; 24 cm
所蔵情報: loading…
2.

図書
図書
2. Artificial intelligence and software engineering : understanding the promise of the future
Derek Partridge
出版情報: Chicago : Glenlake Publishing , Chicago : Fitzroy Dearborn, 1998  ix, 274 p ; 24 cm
所蔵情報: loading…
3.

図書
図書
3. Evolutionary computation : toward a new philosophy of machine intelligence
David B. Fogel
出版情報: New York : IEEE Press, c1995  xv, 272 p. ; 24 cm
所蔵情報: loading…
目次情報: 続きを見る
Preface to the Third Edition
Preface to the Second Edition
Preface to the First Edition
Defining Artificial Intelligence / 1：
Background / 1.1：
The Turing Test / 1.2：
Simulation of Human Expertise / 1.3：
Samuel's Checker Program / 1.3.1：
Chess Programs / 1.3.2：
Expert Systems / 1.3.3：
A Criticism of the Expert Systems or Knowledge-Based Approach / 1.3.4：
Fuzzy Systems / 1.3.5：
Perspective on Methods Employing Specific Heuristics / 1.3.6：
Neural Networks / 1.4：
Definition of Intelligence / 1.5：
Intelligence, the Scientific Method, and Evolution / 1.6：
Evolving Artificial Intelligence / 1.7：
References
Chapter 1 Exercises
Natural Evolution / 2：
The Neo-Darwinian Paradigm / 2.1：
The Genotype and the Phenotype: The Optimization of Behavior / 2.2：
Implications of Wright's Adaptive Topography: Optimization Is Extensive Yet Incomplete / 2.3：
The Evolution of Complexity: Minimizing Surprise / 2.4：
Sexual Reproduction / 2.5：
Sexual Selection / 2.6：
Assessing the Beneficiary of Evolutionary Optimization / 2.7：
Challenges to Neo-Darwinism / 2.8：
Neutral Mutations and the Neo-Darwinian Paradigm / 2.8.1：
Punctuated Equilibrium / 2.8.2：
Summary / 2.9：
Chapter 2 Exercises
Computer Simulation of Natural Evolution / 3：
Early Speculations and Specific Attempts / 3.1：
Evolutionary Operation / 3.1.1：
A Learning Machine / 3.1.2：
Artificial Life / 3.2：
Evolutionary Programming / 3.3：
Evolution Strategies / 3.4：
Genetic Algorithms / 3.5：
The Evolution of Evolutionary Computation / 3.6：
Chapter 3 Exercises
Theoretical and Empirical Properties of Evolutionary Computation / 4：
The Challenge / 4.1：
Theoretical Analysis of Evolutionary Computation / 4.2：
The Framework for Analysis / 4.2.1：
Convergence in the Limit / 4.2.2：
The Error of Minimizing Expected Losses in Schema Processing / 4.2.3：
The Two-Armed Bandit Problem / 4.2.3.1：
Extending the Analysis for "Optimally" Allocating Trials / 4.2.3.2：
Limitations of the Analysis / 4.2.3.3：
Misallocating Trials and the Schema Theorem in the Presence of Noise / 4.2.4：
Analyzing Selection / 4.2.5：
Convergence Rates for Evolutionary Algorithms / 4.2.6：
Does a Best Evolutionary Algorithm Exist? / 4.2.7：
Empirical Analysis / 4.3：
Variations of Crossover / 4.3.1：
Dynamic Parameter Encoding / 4.3.2：
Comparing Crossover to Mutation / 4.3.3：
Crossover as a Macromutation / 4.3.4：
Self-Adaptation in Evolutionary Algorithms / 4.3.5：
Fitness Distributions of Search Operators / 4.3.6：
Discussion / 4.4：
Chapter 4 Exercises
Intelligent behavior / 5：
Intelligence in Static and Dynamic Environments / 5.1：
General Problem Solving: Experiments with Tic-Tac-Toe / 5.2：
The Prisoner's Dilemma: Coevolutionary Adaptation / 5.3：
Evolving Finite-State Representations / 5.3.1：
Learning How to Play Checkers without Relying on Expert Knowledge / 5.4：
Evolving a Self-Learning Chess Player / 5.5：
Chapter 5 Exercises / 5.6：
Perspective / 6：
Evolution as a Unifying Principle of Intelligence / 6.1：
Prediction and the Languagelike Nature of Intelligence / 6.2：
The Misplaced Emphasis on Emulating Genetic Mechanisms / 6.3：
Bottom-Up Versus Top-Down / 6.4：
Toward a New Philosophy of Machine Intelligence / 6.5：
Chapter 6 Exercises
Glossary
Index
About the Author
Intelligent Behavior
Preface to the Third Edition
Preface to the Second Edition
Preface to the First Edition
4.

図書
図書
4. Artificial life : an overview
edited by Christopher G. Langton
出版情報: Cambridge, Mass. : MIT Press, c1995  xi, 340 p. ; 26 cm
シリーズ名: Complex adaptive systems
所蔵情報: loading…
目次情報: 続きを見る
Foreword
Editor's Introduction
Artificial Life as a Tool for Biological Inquiry / 1：
Introduction
Brief Survey of Artificial Life Models Applied to Problems in Biology
The Molecular Level: Wetware Systems / 2.1：
The Cellular Level: Software Systems / 2.2：
The Organism Level: Hardware Systems / 2.3：
Software Life at the Population Level: Equational Models versu s Artificial Life Models / 2.4：
Open Problems in Biology that Are Amenable to Study by Artificia l Life Modeling
Origin of Life and Self-Organization / 3.1：
Cultural Evolution / 3.2：
Origin and Maintenance of Sex / 3.3：
Shifting Balance Paradigm / 3.4：
Fitness and Adaptedness / 3.5：
Structure of Ecosystems / 3.6：
Mind in Nature / 3.7：
References
Cooperation and Community Structure in Artificial Ecosystems
The Evolution of Cooperation / 2：
The Prisoner's Dilemma
Evolutionary Dynamics
Spatial Games
Artificial Community Structure / 3：
Food Webs
Community Models
Artificial Ecologies
Discussion / 4：
Acknowledgments
Extended Molecular Evolutionary Biology: Artificial Life Bridging the Gap Between Chemistry and Biology
Molecular Replication and Template Chemistry
Mutation, Error-propagation, and Optimization
Mutational Stability of Structures
Shape Space Covering
Evolutionary Biotechnology / 5：
The Theory of Evolution and Artificial Life / 6：
Visual Models of Morphogenesis
Features of Models of Morphogenesis
Space-Oriented Models
Reaction-Diffusion Pattern Models
A Reaction-Diffusion Model of Differentiation
Diffusion-Limited Accretive Growth
Diffusion-Limited Aggregation
Cellular Automata
Voxel Automata
Development in Expanding Space
Structure-Oriented Models
L-Systems / 4.1：
Branching Structures with Exogenous Control / 4.2：
Map L-Systems / 4.3：
Mobile Cells in a Continuous Medium / 4.4：
Conclusions
The Artificial Life Roots of Artificial Intelligence
Delineating the Field
The Subject Matter Is Intelligent Behavior
The Methodology Is Based on Building Artificial Systems
Behavior-Oriented Al Is Strongly Influenced by Biology
Behavior-Oriented AI is Complementary to Other Approaches to A
The Rest of the Paper Focuses on Emergence / 2.5：
Behavior Systems
Behavior Systems Should Be Viewed as Living Systems
Some Guidelines Are Known for Designing Behavior Systems
Different Approaches Are Explored for Designing the Behavior P rograms
Neural Networks Approaches / 3.3.1：
Algorithmic Approaches / 3.3.2：
Circuit Approaches / 3.3.3：
Dynamics Approaches / 3.3.4：
Emergent Behavior
Emergence Can Be Defined in Terms of the Need for New Descript ive Categories
The Most Basic Form of Emergent Behavior Is Based on Side Effe cts
A Second Form of Emergent Behavior Is Based on Spatiotemporal Structures
Emergent Functionality
There Are Severe Difficulties in Using Existing Artificial Ne ural Network Techniques or Evolutio... / 5.1：
A Selectionist Approach May Be the Key for Generating Emergen t Functionality / 5.2：
Some Open Issues
Acknowledgment
Toward Synthesizing Artificial Neural Networks that Exhibit Cooperative Intelligent Behavior: Some Open Issues in Artificial Life
Al Versus AL Approach to Cognition
Animal Intelligence: Open Questions in AL
Common Behaviors in Animals
Social Grouping / 3.1.1：
Specialization of Labor / 3.1.2：
Food Finding, Preparation, and Storage / 3.1.3：
Symbiotic Behavior / 3.1.4：
Dominance, Combat, and Territoriality / 3.1.5：
Mate Selection and Mating / 3.1.6：
Nesting / 3.1.7：
Parenting / 3.1.8：
Predation Strategies / 3.1.9：
Predator Avoidance and Defense / 3.1.10：
Dissembling Behaviors / 3.1.11：
Primitive Tool Use and Culture / 3.1.12：
Other Complex Behaviors / 3.1.13：
Animal Cooperation via Communication
Insect Communication / 3.2.1：
Avian Communication / 3.2.2：
Mammalian Communication / 3.2.3：
Primate Communication / 3.2.4：
Cross-Species Communication / 3.2.5：
Animal Development and Learning
Synthesizing Animal Intelligence via Evolution and Learning
Evolution/Learning of Food Discrimination
Evolution of Foraging and Trail Laying
Evolution of Communication
Evolution of Predation and Predator Avoidance
Toward the Synthesis of Protohuman Intelligence / 4.5：
Other Research Issues and Methodological Principles
Principle of Hypothesis-Driven / 5.1.1：
Abstraction Hierarchies
Principle of Minimal Effective Embodiment / 5.1.2：
Principle of Midpoint Entry / 5.1.3：
Principle of Indirectness / 5.1.4：
Principle of Naturalness / 5.1.5：
Modeling Adaptive Autonomous Agents
What is an Adaptive Autonomous Agent?
Guiding Principles
Characteristics of Agent Architectures
Task-Oriented Modules
Task-Specific Solutions
Role of Representations is Deemphasized
Decentralized Control Structure
Goal-Directed Activity is an Emergent Property
Role for Learning and Development / 4.6：
Some Example Autonomous Agents
A Mobile Robot
An Interface Agent
A Scheduling System / 5.3：
Overview of the State of the Art
Action Selection / 6.1：
The Problem / 6.1.1：
Progress Made / 6.1.2：
Open Problems / 6.1.3：
Learning from Experience / 6.2：
Chaos as a Source of Complexity and Diversity in Evolution / 6.2.1：
Complexity, Diversity, and Emergence
Edge of Chaos in an Imitation Game: Chaos as a Source of Comple xity
Key Concept for the Origin of Complexity and Diversity: Dynamic Clustering in Networks of Chaotic ...
Clustering in Hypercubic Coupled Maps: Self-organizing Genetic Algorithms
I-Bit Clustering
2-Bit Clustering
Parity Check Clustering
Maintenance of Diversity and Dynamic Stability: Homeochaos
Source of Novelty and Growth of Diversity: Open Chaos
Beyond Top-Down and Bottom-Up Approaches
Conclusion
An Evolutionary Approach to Synthetic Biology: Zen and the Art of Creating Life
Synthetic Biology
Recognizing Life
What Natural Evolution Does
Evolution in Sequence Space
Natural Evolution in an Artificial Medium
The Approach
The Computational Medium
The Genetic Language
Genetic Operators
Mutations / 7.1：
Flaws / 7.2：
Recombination-Sex / 7.3：
The Nature of Sex / 7.3.1：
Implementation of Digital Sex / 7.3.2：
Transposons / 7.4：
Artificial Death / 8：
Operating System / 9：
Spatial Topology / 10：
Ecological Context / 11：
The Living Environment / 11.1：
Diversity / 11.2：
Ecological Attractors / 11.3：
Cellularity / 12：
Multicellularity / 13：
Biological Perspective-Cambrian Explosion / 13.1：
Computational Perspective--Parallel Processes / 13.2：
Evolution as a Proven Route / 13.3：
Fundamental Definition / 13.4：
Computational Implementation / 13.5：
Digital "Neural Networks"--Natural Artificial Intelligence / 13.6：
Digital Husbandry / 14：
Living Together / 15：
Challenges / 16：
Beyond Digital Naturalism
Life and the Organization Problem in Biology
Replicator Equations Without Replicators
Organizations Must be Constructed
Organization--De Arte Combinatoria1
Constructive Part
Dynamical Part
Level 0 / 4.2.1：
Level 1 / 4.2.2：
Level 2 / 4.2.3：
Biology / 4.2.4：
A functional perpetuum mobile
ALife and Real Life
Sources
Learning About Life
New Ways of Thinking
Tools for Learning
Learning Experiences
LEGO/Logo Creatures
StarLogo Termites
Decentralized Thinking
Positive Feedback Isn't Always Negative
Randomness Can Help Create Order
A Flock Isn't a Big Bird
A Traffic Jam Isn't Just a Collection of Cars / 5.4：
The Hills are Alive / 5.5：
Book Reviews: Books on Artificial Life and Related Topics
Computer Viruses as Artificial Life
What Is a Computer Virus?
Related Software
Virus Structure and Operation
Evolution of Viruses
First Generation: Simple
Second Generation: Self-Recognition
Third Generation: Stealth
Fourth Generation: Armored
Fifth Generation: Polymorphic
Defenses and Outlook
Viruses as Artificial Life
Viruses as Patterns in Space-Time
Self-Reproduction of Viruses
Information Storage of a Self-Representation / 6.3：
Virus Metabolism / 6.4：
Functional Interactions with the Virus's Environment / 6.5：
Interdependence of Virus Parts / 6.6：
Virus Stability Under Perturbations / 6.7：
Virus Evolution / 6.8：
Growth / 6.9：
Other Behavior / 6.10：
Concluding Comments
Genetic Algorithms and Artificial Life
Overview of Genetic Algorithms
Interactions Between Learning and Evolution
The Baldwin effect
Capturing the Baldwin Effect in a Simple Model
Evolutionary Reinforcement Learning (ERL)
Ecosystems and Evolutionary Dynamics
Echo
Measuring Evolutionary Activity
Learning Classifier Systems
Immune Systems
Social Systems
Open Problems and Future Directions
Suggested Reading
Artificial Life as Philosophy
Levels of Functional Equivalence in Reverse Bioengineering
What Is Life?
Virtual Life
Synthetic Life
Why Do We Need Artificial Life?
The Many Lives of Artificial Life
Artificial (Way of) Life
Synthesis
A Matter of Levels
On the Nature of Phenomenological Analogies
AL Lost in Immensity
Reductionism and the Nature of Artificial Life
Boundary Conditions
More on Reductionists and Environments
Function as a Side Effect of Structure?
Computational Reductionism
The Pride of Being Reductionist
Why Do We Need AL?
AL and Theoretical Biology
The Interplay of AL and Philosophy
Designing Artificial Problem-Solvers
AL and Art
Index
Foreword
Editor's Introduction
Artificial Life as a Tool for Biological Inquiry / 1：
5.

図書
図書
5. Artificial minds
Stan Franklin
出版情報: Cambridge, Mass. : MIT Press, c1995  xi, 449 p. ; 24 cm
所蔵情報: loading…
6.

図書
図書
6. Natural language understanding. 2nd ed (: pbk)
James Allen
出版情報: Redwood City, Calif. : Benjamin/Cummings Pub. Co., c1995  xv, 654 p. ; 24 cm
所蔵情報: loading…
目次情報: 続きを見る
Introduction to Natural Language Understanding / 1：
The Study of Language
Applications of Natural Language Understanding
Evaluating Language Understanding Systems
The Different Levels of Language Analysis
Representations and Understanding
The Organization of Natural Language Understanding Systems
Linguistic Background: An Outline of English Syntax / 2：
Words
The Elements of Simple Noun Phrases
Verb Phrases and Simple Sentences
Noun Phrases Revisited
Adjective Phrases
Adverbial Phrases
Grammars and Parsing / 3：
Grammars and Sentence Structure
What Makes a Good Grammar
A Top-Down Parser
A Bottom-Up Chart Parser
Top-Down Chart Parsing
Finite State Models and Morphological Processing
Grammars and Logic Programming
Features and Augmented Grammars / 4：
Feature Systems and Augmented Grammars
Some Basic Feature Systems for English
Morphological Analysis and the Lexicon
A Simple Grammar Using Features
Parsing with Features
Augmented Transition Networks
Definite Clause Grammars
Generalized Feature Systems and Unification Grammars
Grammars for Natural Language / 5：
Auxiliary Verbs and Verb Phrases
Movement Phenomena in Language
Handling Questions in Context-Free Grammars
Noun Phrases and Relative Clauses
The Hold Mechanism in ATN
Gap Threading
Toward Efficient Parsing / 6：
Human Preferences in Parsing
Encoding Uncertainty: Shift-Reduce Parsers
A Deterministic Parser
Techniques for Efficient Encoding of Ambiguity
Partial Parsing
Ambiguity Resolution: Statistical Methods / 7：
Basic Probability Theory
Estimating Probabilities
Part-of-Speech Tagging
Obtaining Lexical Probabilities
Probabilistic Context-Free Grammars
Best-First Parsing
A Simple Context-Dependent Best-First Parser
Semantics and Logical Form / 8：
Introduction to Natural Language Understanding / 1：
The Study of Language
Applications of Natural Language Understanding
7.

図書
図書
7. Vision, instruction, and action
David Chapman
出版情報: Cambridge, Mass. : MIT Press, 1991  ix, 295 p. ; 24 cm
シリーズ名: The MIT Press series in artificial intelligence
所蔵情報: loading…
8.

図書
図書
8. Data fusion in robotics and machine intelligence
edited by Mongi A. Abidi, Rafael C. Gonzalez
出版情報: Boston ; Tokyo : Academic Press, c1992  xii, 546 p. ; 24 cm
所蔵情報: loading…
目次情報: 続きを見る
Introduction
Data Fusion and Sensor Integration: State of the Art 1990s
Multi-Source Spatial Fusion Using Bayesian Reasoning
Multi-Sensor Strategies Using Dempster/Shafer Belief Accumulation
Data Fusion Techniques Using Robust Statistics
Recursive Fusion Operators: Desirable Properties and Illustrations
Distributed Data Fusion Using Kalman Filtering
Least-Squares Fusion of Multi-Sensory Data
Fusion of Multi-Dimensional Data Using Regularization
Geometric Fusion: Minimizing Uncertainty Ellipsoid Volumes
Combination of Fuzzy Information in the Framework of Possibility Theory
Data Fusion: A Neural Networks Implementation
Introduction
Data Fusion and Sensor Integration: State of the Art 1990s
Multi-Source Spatial Fusion Using Bayesian Reasoning
9.

図書
図書
9. Artificial intelligence in design (: UK ; : US)
edited by D.T. Pham
出版情報: London : Springer, 1991  xviii, 499 p. ; 24 cm
シリーズ名: Artificial intelligence in industry
所蔵情報: loading…
10.

図書
図書
10. A new guide to artificial intelligence (case ; pbk.)
Derek Partridge
出版情報: Norwood, N.J. : Ablex Pub. Corp., c1991  xxi, 546 p. ; 24 cm
シリーズ名: Ablex series in computational sciences
所蔵情報: loading…
文献の複写および貸借の依頼を行う
 文献複写・貸借依頼