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| Preface | |
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| Problems and Search | |
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| What Is Artificial Intelligence? | |
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| The AI Problems | |
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| The Underlying Assumption | |
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| What Is an AI Technique? | |
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| The Level of the Model | |
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| Criteria for Success | |
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| Some General References | |
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| One Final Word | |
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| Exercises | |
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| Problems, Problem Spaces, and Search | |
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| Defining the Problem as a State Space Search | |
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| Production Systems | |
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| Problem Characteristics | |
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| Production System Characteristics | |
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| Issues in the Design of Search Programs | |
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| Additional Problems | |
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| Summary | |
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| Exercises | |
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| Heuristic Search Techniques | |
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| Generate-and-Test | |
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| Hill Climbing | |
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| Best-First Search | |
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| Problem Reduction | |
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| Constraint Satisfaction | |
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| Means-Ends Analysis | |
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| Summary | |
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| Exercises | |
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| Knowledge Representation | |
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| Knowledge Representation Issues | |
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| Representations and Mappings | |
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| Approaches to Knowledge Representation | |
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| Issues in Knowledge Representation | |
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| The Frame Problem | |
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| Summary | |
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| Using Predicate Logic | |
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| Representing Simple Facts in Logic | |
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| Representing Instance and Isa Relationships | |
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| Computable Functions and Predicates | |
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| Resolution | |
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| Natural Deduction | |
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| Summary | |
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| Exercises | |
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| Representing Knowledge Using Rules | |
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| Procedural versus Declarative Knowledge | |
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| Logic Programming | |
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| Forward versus Backward Reasoning | |
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| Matching | |
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| Control Knowledge | |
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| Summary | |
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| Exercises | |
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| Symbolic Reasoning under Uncertainty | |
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| Introduction to Nonmonotonic Reasoning | |
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| Logics for Nonmonotonic Reasoning | |
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| Implementation Issues | |
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| Augmenting a Problem Solver | |
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| Implementation: Depth-First Search | |
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| Implementation: Breadth-First Search | |
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| Summary | |
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| Exercises | |
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| Statistical Reasoning | |
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| Probability and Bayes' Theorem | |
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| Certainty Factors and Rule-Based Systems | |
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| Bayesian Networks | |
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| Dempster-Shafer Theory | |
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| Fuzzy Logic | |
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| Summary | |
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| Exercises | |
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| Weak Slot-and-Filler Structures | |
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| Semantic Nets | |
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| Frames | |
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| Exercises | |
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| Strong Slot-and-Filler Structures | |
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| Conceptual Dependency | |
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| Scripts | |
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| CYC | |
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| Exercises | |
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| Knowledge Representation Summary | |
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| Syntactic-Semantic Spectrum of Representation | |
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| Logic and Slot-and-Filler Structures | |
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| Other Representational Techniques | |
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| Summary of the Role of Knowledge | |
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| Exercises | |
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| Advanced Topics | |
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| Game Playing | |
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| Overview | |
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| The Minimax Search Procedure | |
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| Adding Alpha-Beta Cutoffs | |
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| Additional Refinements | |
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| Iterative Deepening | |
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| References on Specific Games | |
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| Exercises | |
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| Planning | |
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| Overview | |
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| An Example Domain: The Blocks World | |
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| Components of a Planning System | |
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| Goal Stack Planning | |
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| Nonlinear Planning Using Constraint Posting | |
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| Hierarchical Planning | |
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| Reactive systems | |
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| Other Planning Techniques | |
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| Exercises | |
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| Understanding | |
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| What Is Understanding? | |
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| What Makes Understanding Hard? | |
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| Understanding as Constraint Satisfaction | |
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| Summary | |
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| Exercises | |
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| Natural Language Processing | |
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| Introduction | |
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| Syntactic Processing | |
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| Semantic Analysis | |
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| Discourse and Pragmatic Processing | |
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| Summary | |
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| Exercises | |
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| Parallel and Distributed AI | |
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| Psychological Modeling | |
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| Parallelism in Reasoning Systems | |
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| Distributed Reasoning Systems | |
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| Summary | |
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| Exercises | |
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| Learning | |
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| What Is Learning? | |
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| Rote Learning | |
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| Learning by Taking Advice | |
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| Learning in Problem Solving | |
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| Learning from Examples: Induction | |
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| Explanation-Based Learning | |
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| Discovery | |
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| Analogy | |
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| Formal Learning Theory | |
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| Neural Net Learning and Genetic Learning | |
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| Summary | |
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| Exercises | |
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| Connectionist Models | |
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| Introduction: Hopfield Networks | |
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| Learning in Neural Networks | |
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| Applications of Neural Networks | |
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| Recurrent Networks | |
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| Distributed Representations | |
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| Connectionist AI and Symbolic AI | |
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| Exercises | |
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| Common Sense | |
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| Qualitative Physics | |
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| Commonsense Ontologies | |
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| Memory Organization | |
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| Case-Based Reasoning | |
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| Exercises | |
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| Expert Systems | |
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| Representing and Using Domain Knowledge | |
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| Expert System Shells | |
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| Explanation | |
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| Knowledge Acquisition | |
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| Summary | |
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| Exercises | |
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| Perception and Action | |
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| Real-Time Search | |
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| Perception | |
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| Action | |
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| Robot Architectures | |
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| Summary | |
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| Exercises | |
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| Conclusion | |
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| Components of an AI Program | |
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| Exercises | |
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| References | |
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| Acknowledgements | |
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| Author Index | |
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| Subject Index | |