| 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： |
図書
