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| Preface 1 Introduction 1.1 What is AI? 1.2 Approaches to Artificial Intelligence 1.3 Brief History of AI 1.4 Plan of the Book 1.5 Additional Readings and Discussion I Reactive Machines 2 Stimulus-Response Agents 2.1 Perception and Action 2.2 Representing and Implementing Action Functions 2.3 Additional Readings and Discussion 3 Neural Networks 3.1 Introduction 3.2 Training Single TLUs 3.3 Neural Networks 3.4 Generalization, Accuracy, and Overfitting 3.5 Additional Readings and Discussion 4 Machine Evolution 4.1 Evolutionary Computation 4.2 Genetic Programming 4.3 Additional Readings and Discussion 5 State Machines 5.1 Representing the Environment by Feature Vectors 5.2 Elman Networks 5.3 Iconic Representations 5.4 Blackboard Systems 5.5 Additional Readings and Discussion 6 Robot Vision 6.1 Introduction 6.2 Steering a Van 6.3 Two Stages of Robot Vision 6.4 Image Processing 6.5 Scene Analysis 6.6 Stereo Vision 6.7 Additional Readings and Discussion II Search in State Spaces 7 Agents that Plan 7.1 Memory Versus Computation 7.2 State-Space Graphs 7.3 Searching Explicit State Spaces 7.4 Feature-Based State Spaces 7.5 Graph Notation 7.6 Additional Readings and Discussion 8 Uninformed Search 8.1 Formulating the State Space 8.2 Components of Implicit State-Space Graphs 8.3 Breadth-First Search 8.4 Depth-First or Bracktracking Search 8.5 Iterative Deepening 8.6 Additional Readings and Discussion 9 Heuristic Search 9.1 Using Evaluation Functions 9.2 A General Graph-Searching Algorithm 9.3 Heuristic Functions and Search Efficiency 9.4 Additional Readings and Discussion 10 Planning, Acting, and Learning 10.1 The Sense/Plan/Act Cycle 10.2 Approximate Search 10.3 Learning Heuristic Functions 10.4 Rewards Instead of Goals 10.5 Additional Readings and Discussion 11 Alternative Search Formulations and Applications 11.1 Assignment Problems 11.2 Constructive Methods 11.3 Heuristic Repair 11.4 Function Optimization 12 Adversarial Search 12.1 Two-Agent Games 12.2 The Minimax Procedure 12.3 The Alpha-Beta Procedure 12.4 The Search Efficiency of the Alpha-Beta Procedure 12.5 Other Important Matters 12.6 Games of Chance 12.7 Learning Evaluation Functions 12.8 Additional Readings and Discussion III Knowledge Representation and Reasoning 13 The Propositional Calculus 13.1 Using Constraints on Feature Values 13.2 The Language 13.3 Rules of Inference 13.4 Definition of Proof 13.5 Semantics 13.6 Soundness and Completeness 13.7 The PSAT Problem 13.8 Other Important Topics 14 Resolution in The Propositional Calculus 14.1 A New Rule of Inference: Resolution 14.2 Converting Arbitrary wffs to Conjunctions of Clauses 14.3 Resolution Refutations 14.4 Resolution Refutation Search Strategies 14.5 Horn Clauses 15 The Predicate Calculus 15.1 Motivation 15.2 The Language and its Syntax 15.3 Semantics 15.4 Quantification 15.5 Semantics of Quantifiers 15.6 Predicate Calculus as a Language for Representing Knowledge 15.7 Additional Readings and Discussion 16 Resolution in the Predicate Calculus 16.1 Unification 16.2 Predicate-Calculus Resolution 16.3 Completeness and Soundness 16.4 Converting Arbitrary wffs to Clause Form 16.5 Using Resolution to Prove Theorems 16.6 Answer Extraction 16.7 The Equality Predicate 16.8 Additional Readings and Discussion 17 Knowledge-Based Systems 17.1 Confronting the Real World 17.2 Reasoning Using Horn Clauses 17.3 Maintenance in Dynamic Knowledge Bases 17.4 Rule-Based Expert Systems 17.5 Rule Learning 17.6 Additional Readings and Discussion 18 Representing Commonsense Knowledge 18.1 The Commonsense World 18.2 Time 18.3 Knowledge Representation by Networks 18.4 Additional Readings and Discussion 19 Reasoning with Uncertain Information 19.1 Review of Probability Theory 19.2 Probabilistic Inference 19.3 Bayes Networks 19.4 Patterns of Inference in Bayes Networks 19.5 Uncertain Evidence 19.6 D-Seperation 19.7 Probabilistic Inference in Polytrees 19.8 Additional Readings and Discussion 20 Learning and Acting with Bayes Nets 20.1 Learning Bayes Nets 20.2 Probabilistic Inference and Action 20.3 Additional Readings and Discussion IV Planning Method Based on Logic 21 The Situation Calculus 21.1 Reasoning about States and Actions 21.2 Some Difficulties 21.3 Generating Plans 21.4 Additional Reading and Discussion 22 Planning 22.1 STRIPS Planning Systems 22.2 Plan Spaces and Partial-Order Planning 22.3 Hierarchical Planning 22.4 Learning Plans' 22.5 Additional Readings and Discussion V Communication and Integration 23 Multiple Agents 23.1 Interacting Agents 23.2 Models of Other Agents 23.3 A Modal Logic of Knowledge 23.4 Additional Readings and Discussion 24 Communication Among Agents 24.1 Speech Acts 24.2 Understanding Language Strings 24.3 Efficient Communication 24.4 Natural Language Processing 24.5 Additional Readings and Discussion 25 Agent Architectures 25.1 Three-Level Architectures 25.2 Goal Arbitration 25.3 The Triple-Tower Architecture 25.4 Bootstrapping 25.5 Additional Readings and Discussion |
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