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1.

図書

図書
Derek Partridge
出版情報: Chicago : Glenlake Publishing , Chicago : Fitzroy Dearborn, 1998  ix, 274 p ; 24 cm
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2.

図書

図書
S.K. Pal, A. Skowron, editors
出版情報: Singapore ; New York : Springer Verlag, 1999  xiv, 454 p. ; 24 cm
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3.

図書

図書
Stan Franklin
出版情報: Cambridge, Mass. : MIT Press, c1995  xi, 449 p. ; 24 cm
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4.

図書

図書
Eugene Charniak
出版情報: Cambridge, Mass. : MIT Press, c1993  xx, 170 p. ; 24 cm
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5.

図書

図書
edited by Dov M. Gabbay, and C.J. Hogger and J.A. Robinson ; volume co-ordinator, J. Siekmann
出版情報: Oxford : Clarendon Press , Oxford ; New York : Oxford University Press, 1994  xv, 511 p. ; 24 cm
シリーズ名: Handbook of logic in artificial intelligence and logic programming / edited by Dov M. Gabbay, and C.J. Hogger and J.A. Robinson ; v. 2
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Automated Reasoning / L. Wos ; R. Veroff2.1:
General Unification Theory / J. Siekmann2.2:
Induction / Ch. Walther2.3:
Higher-Order Features, Types and Fixpoints / D. Leivant2.4:
Metalanguages, Reflection Principles and Self Reference / D. Perlis ; V.S. Subrahmanian2.5:
Classical vs non-classical Logic / D. Gabbay2.6:
Automated Reasoning / L. Wos ; R. Veroff2.1:
General Unification Theory / J. Siekmann2.2:
Induction / Ch. Walther2.3:
6.

図書

図書
edited by Dov M. Gabbay, and C.J. Hogger and J.A. Robinson ; volume co-ordinator, D. Nute
出版情報: Oxford : Clarendon Press , Oxford ; New York : Oxford University Press, 1994  xix, 529 p. ; 24 cm
シリーズ名: Handbook of logic in artificial intelligence and logic programming / edited by Dov M. Gabbay, and C.J. Hogger and J.A. Robinson ; v. 3
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AI and Nonmonotonic Reasoning / M. Ginsberg3.1:
General Non monotonic Logic / D. Makinson3.2:
Some direct theories of nonmonotonic Inheritance / J. Horty3.3:
Default Logic / D. Poole3.4:
Autoepistemic Logic / K. Konolige3.5:
Circumscription / V. Lifschitz3.6:
Defeasible Logic / D. Nute3.7:
Probabilistic Logic / H. Kyburg3.8:
Possibilistic Logic / 3.9:
AI and Nonmonotonic Reasoning / M. Ginsberg3.1:
General Non monotonic Logic / D. Makinson3.2:
Some direct theories of nonmonotonic Inheritance / J. Horty3.3:
7.

図書

図書
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
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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:
8.

図書

図書
Erik Sandewall
出版情報: Oxford : Clarendon Press , New York : Oxford University Press, 1994-  v. ; 25 cm
シリーズ名: Oxford logic guides ; 30
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Inert and inhabited dynamical systems / 1:
Inference operations on scenario descriptions / 2:
Underlying semantics for IDS worlds / 3:
Elementary feature logic and meta-logical concepts / 4:
Lexical-domain object-feature logic / 5:
Temporal feature logic for discrete time domains / 6:
Chronicle completion in k-IA / 7:
Intended models for chronicles in k-IA / 8:
Entailment methods for k-IA using DFL-1 / 9:
Duration constraints / 10:
Entailment methods for k-IA using occlusion / 11:
Composite actions / 12:
Upper applicability bounds and assessment of soundness / 13:
Future directions / 14:
Term index / A:
Notation / B:
References to related work / C:
Inert and inhabited dynamical systems / 1:
Inference operations on scenario descriptions / 2:
Underlying semantics for IDS worlds / 3:
9.

図書

図書
David B. Fogel
出版情報: New York : IEEE Press, c1995  xv, 272 p. ; 24 cm
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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
10.

図書

図書
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
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