Artificial intelligence

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Artificial intelligence / Elaine Rich

資料種別:: 図書
出版情報:: New York ; Auckland : McGraw-Hill, c1983
形態:: xii, 436 p. ; 24 cm
シリーズ名:: McGraw-Hill series in artificial intelligence <BA00149879>
著者名:: Rich, Elaine <DA00061515>
ISBN:: 9780070522619 [0070522618]
書誌ID:: BA00383242

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注記:

Bibliography: p. 412-426
Includes indexes

主題:

人工頭脳

言語:

英語

目次情報:

Preface

Problems and Search / I：

What Is Artificial Intelligence? / 1：

The AI Problems / 1.1：

The Underlying Assumption / 1.2：

What Is an AI Technique? / 1.3：

The Level of the Model / 1.4：

Criteria for Success / 1.5：

Some General References / 1.6：

One Final Word / 1.7：

Exercises / 1.8：

Problems, Problem Spaces, and Search / 2：

Defining the Problem as a State Space Search / 2.1：

Production Systems / 2.2：

Problem Characteristics / 2.3：

Production System Characteristics / 2.4：

Issues in the Design of Search Programs / 2.5：

Additional Problems / 2.6：

Summary / 2.7：

Heuristic Search Techniques / 2.8：

Generate-and-Test / 3.1：

Hill Climbing / 3.2：

Best-First Search / 3.3：

Problem Reduction / 3.4：

Constraint Satisfaction / 3.5：

Means-Ends Analysis / 3.6：

Knowledge Representation / 3.7：

Knowledge Representation Issues / 4：

Representations and Mappings / 4.1：

Approaches to Knowledge Representation / 4.2：

Issues in Knowledge Representation / 4.3：

The Frame Problem / 4.4：

Using Predicate Logic / 4.5：

Representing Simple Facts in Logic / 5.1：

Representing Instance and Isa Relationships / 5.2：

Computable Functions and Predicates / 5.3：

Resolution / 5.4：

Natural Deduction / 5.5：

Representing Knowledge Using Rules / 5.6：

Procedural versus Declarative Knowledge / 6.1：

Logic Programming / 6.2：

Forward versus Backward Reasoning / 6.3：

Matching / 6.4：

Control Knowledge / 6.5：

Symbolic Reasoning under Uncertainty / 6.6：

Introduction to Nonmonotonic Reasoning / 7.1：

Logics for Nonmonotonic Reasoning / 7.2：

Implementation Issues / 7.3：

Augmenting a Problem Solver / 7.4：

Implementation: Depth-First Search / 7.5：

Implementation: Breadth-First Search / 7.6：

Statistical Reasoning / 7.7：

Probability and Bayes' Theorem / 8.1：

Certainty Factors and Rule-Based Systems / 8.2：

Bayesian Networks / 8.3：

Dempster-Shafer Theory / 8.4：

Fuzzy Logic / 8.5：

Weak Slot-and-Filler Structures / 8.6：

Semantic Nets / 9.1：

Frames / 9.2：

Strong Slot-and-Filler Structures / 9.3：

Conceptual Dependency / 10.1：

Scripts / 10.2：

CYC / 10.3：

Knowledge Representation Summary / 10.4：

Syntactic-Semantic Spectrum of Representation / 11.1：

Logic and Slot-and-Filler Structures / 11.2：

Other Representational Techniques / 11.3：

Summary of the Role of Knowledge / 11.4：

Advanced Topics / 11.5：

Game Playing / 12：

Overview / 12.1：

The Minimax Search Procedure / 12.2：

Adding Alpha-Beta Cutoffs / 12.3：

Additional Refinements / 12.4：

Iterative Deepening / 12.5：

References on Specific Games / 12.6：

Planning / 12.7：

An Example Domain: The Blocks World / 13.1：

Components of a Planning System / 13.3：

Goal Stack Planning / 13.4：

Nonlinear Planning Using Constraint Posting / 13.5：

Hierarchical Planning / 13.6：

Reactive systems / 13.7：

Other Planning Techniques / 13.8：

Understanding / 13.9：

What Is Understanding? / 14.1：

What Makes Understanding Hard? / 14.2：

Understanding as Constraint Satisfaction / 14.3：

Natural Language Processing / 14.4：

Introduction / 15.1：

Syntactic Processing / 15.2：

Semantic Analysis / 15.3：

Discourse and Pragmatic Processing / 15.4：

Parallel and Distributed AI / 15.5：

Psychological Modeling / 16.1：

Parallelism in Reasoning Systems / 16.2：

Distributed Reasoning Systems / 16.3：

Learning / 16.4：

What Is Learning? / 17.1：

Rote Learning / 17.2：

Learning by Taking Advice / 17.3：

Learning in Problem Solving / 17.4：

Learning from Examples: Induction / 17.5：

Explanation-Based Learning / 17.6：

Discovery / 17.7：

Analogy / 17.8：

Formal Learning Theory / 17.9：

Neural Net Learning and Genetic Learning / 17.10：

Connectionist Models / 17.11：

Introduction: Hopfield Networks / 18.1：

Learning in Neural Networks / 18.2：

Applications of Neural Networks / 18.3：

Recurrent Networks / 18.4：

Distributed Representations / 18.5：

Connectionist AI and Symbolic AI / 18.6：

Common Sense / 18.7：

Qualitative Physics / 19.1：

Commonsense Ontologies / 19.2：

Memory Organization / 19.3：

Case-Based Reasoning / 19.4：

Expert Systems / 19.5：

Representing and Using Domain Knowledge / 20.1：

Expert System Shells / 20.2：

Explanation / 20.3：

Knowledge Acquisition / 20.4：

Perception and Action / 20.5：

Real-Time Search / 21.1：

Perception / 21.2：

Action / 21.3：

Robot Architectures / 21.4：

Conclusion / 21.5：

Components of an AI Program / 22.1：

References / 22.2：

Acknowledgements

Author Index

Subject Index

Preface

Problems and Search / I：

What Is Artificial Intelligence? / 1：

The AI Problems / 1.1：

The Underlying Assumption / 1.2：

What Is an AI Technique? / 1.3：

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