000			09359nam a2200145 4500
020			_a9788181471901)pb)
040			_cCUS
082			_a006.3 _bNIL/A
100			_aNils j. Nilsson
245			_aArtificial intelligence; a new synthesis / _cNilsJ, Nilsson
260			_aBurlington: _bMorgan Kaufmann , _c1998.
300			_axxi, 513 p. _bill. ;
505			_a Introduction 1.1 What Is AI? 1.2 Approaches to Artificial Intelligence 1.5 Brief History of AI 1.4 Plan of the Book 1.5 Additional Readings and Discussion Exercises Reactive Machines Stimulus-Response Agents 2.1 Perception and Action 2.1.1 Perception 2.1.2 Action 2.1.3 Boolean Algebra 2.1.4 Classes and Forms of Boolean Functions 2.2 Representing and Implementing Action Functions 2.2.1 Production Systems 2.2.2 Networks 2.2.3 The Subsumption Architecture 2.3 Additional Readings and Discussion Exercises 3 ^ Neural Networks 5.1 Introduction 3.2 Training Single TLUs 3.2.1 TLU Geometry 3.2.2 Augmented Vectors 3.2.3 Gradient Descent Methods 3.2.4 The Widrow-Hoff Procedure 3.2.5 The Generalized Delta Procedure 3.2.6 The Error-Correction Procedure 3.3 Neural Networks 3.3.1 Motivation 3.3.2 Notation 33.3 The Backpropagation Method 3.3.4 Computing Weight Changes in the Final Layer 3.3.5 Computing Changes to the Weights in Intermediate Layers 3.4 Generalization, Accuracy, and Overfitting 3.5 Additional Readings and Discussion Exercises Machine Evolution 4.1 Evolutionary Computation 4.2 Genetic Programming 4.2.1 Program Representation in GP Contents 4.2.2 The GP Process 4.2.5 Evolving a Wall-Following Robot 4.3 Additional Readings and Discussion Exercises BFp^H 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 Exercises Robot Vision 6.1 Introduction 6.2 Steering an Automobile 6.3 Two Stages of Robot Vision 6.4 Image Processing 6.4.1 Averaging 6.4.2 Edge Enhancement 6.4.3 Combining Edge Enhancement with Averaging 6.4.4 Region Finding 6.4.5 Using Image Attributes Other Than Intensity 6.5 Scene Analysis 6.5.1 Interpreting Lines and Curves in the Image 6.5.2 Model-Based Vision 6.6 Stereo Vision and Depth Information 6.7 Additional Readings and Discussion Exercises w Contents II Search in State Spaces 7L :• 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 Exercises 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 Backtracking Search 8.5 Iterative Deepening 8.6 Additional Readings and Discussion Exercises ^ 9 J Heuristic Search 9.1 Using Evaluation Functions 9.2 A General Graph-Searching Algorithm 9.2.1 Algorithm A* 9.2.2 Admissibility of A* 9.2.5 The Consistency (or Monotone) Condition 9.2.4 Iterative-Deepening A* 9.2.5 Recursive Best-First Search Ff7<iVT.l Contents 9.5 Heuristic Functions and Search Efficiency 9.4 Additionad Readings and Discussion Exercises Planning, Acting, and Learning 10.1 The Sense/Plan/Act Cycle 10.2 Approximate Search 10.2.1 Island-Driven Search 10.2.2 Hierarchical Search 10.2.5 Limited-Horizon Search 10.2.4 Cycles 10.2.5 Building Reactive Procedures 10.3 Learning Heuristic Functions 10.3.1 Explicit Graphs 10.3.2 Implicit Graphs 10.4 Rewards Instead of Goals 10.5 Additional Readings and Discussion Exercises Alternative Search Formulations and Applications 11.1 Assignment Problems 11.2 Constructive Methods 11.3 Heuristic Repair 11.4 Function Optimization Exercises j|2 Adversarial Search 12.1 Two-Agent Games 12.2 The Minimax Procedure xii Contents 12.5 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 Exercises III Knowledge Representation and Reasoning 21; 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 13.5.1 Interpretations 13.5.2 The Propositional Truth Table 13.5.3 Satisfiability and Models 13.5.4 Validity 13.5.5 Equivalence 13.5.6 Entailment 13.6 Soundness and Completeness 13.7 The PSAT Problem 13.8 Other Important Topics 13.8.1 Language Distinctions 13.8.2 Metatheorems 13.8.3 Associative Laws 13.8.4 Distributive Laws Exercises I Resolution in the Propositional Calculns 14.1 A New Rule of Inference: Resolution 14.1.1 Clauses as wffs 14.1.2 Resolution on Clauses 14.1.5 Soundness of Resolution - 14.2 Converting Arbitrary wffs to Conjunctions of Clauses 14.5 Resolution Refutations 14.4 Resolution Refutation Search Strategies 14.4.1 Ordering Strategies 14.4.2 Refinement Strategies 14.5 Horn Clauses Exercises The Predicate Calculus 15.1 Motivation 15.2 The Language and Its Syntax 15.5 Semantics 15.3.1 Worlds 15.3.2 Interpretations 15.3.3 Models and Related Notions 15.3.4 Knowledge 15.4 Quantification 15.5 Semantics of Quantifiers 15.5.1 Universal Quantifiers 15.5.2 Existential Quantifiers 15.5.3 Useful Equivalences 15.5.4 Rules of Inference 15.6 Predicate Calculus as a Language for Representing Knowledge 15.6.1 Conceptualizations 15.6.2 Examples 15.7 Additional Readings and Discussion Exercises Resolution in the Predicate Calculus 16.1 Unification 16J2 Predicate-Calculus Resolution 16.5 Completeness and Soundness 16.4 Converting Arbitrary wffs to Clause Form 16.5 Using Resolution to Prove Theorems 16.6 Answer Extraction 16.7 Hie Equality Predicate 16.8 Additional Readings and Discussion Exercises Knowledge-Based Systems 17.1 Confronting the Real World 17.2 Reasoning Using Horn Clauses 17.3 Maintenance in Dynamic Knowledge Bases V7A Rule-Based Expert Systems 17.5 Rule Learning 17.5.1 Learning Propositional Calculus Rules 17.5.2 Learning First-Order Logic Rules 17.5.3 Explanation-Based Generalization 17.6 Additional Readings and Discussion Exercises ' J Representing Commonsense Knowledge 18.1 The Commonsense World 18.1.1 What Is Commonsense Knowledge? 18.1.2 Difficulties in Representing Commonsense Knowledge 18.1.3 The Importance of Commonsense Knowledge 18.1.4 Research Areas 18.2 Time 18.5 Knowledge Representation by Networks 18.5.1 Taxonomic Knowledge 18.3.2 Semantic Networks 18.3.3 Nonmonotonic Reasoning in Semantic Networks 18.3.4 Frames 18.4 Additional Readings and Discussion Exercises Reasoning with Uncertain Information 19.1 Review of Probability Theory 19.1.1 Fundamental Ideas 19.1.2 Conditional Probabilities 19.2 Probabilistic Inference 19.2.1 A General Method 19.2.2 Conditional Independence 19.5 Bayes Networks 19.4 Patterns of Inference in Bayes Networks 19.5 Uncertain Evidence 19.6 D-Separation 19.7 Probabilistic Inference in Polytrees 19.7.1 Evidence Above 19.7.2 Evidence Below 19.7.3 Evidence Above and Below 19.7.4 A Numerical Example 19.8 Additional Readings and Discussion Exercises lOlg] Learning and Acting with Bayes Nets 20.1 Learning Bayes Nets 20.1.1 Known Network Structure 20.1.2 Learning Network Structure 20.2 Probabilistic Inference and Action 20.2.1 The General Setting 20.2.2 An Extended Example 20.2.3 Generalizing the Example 20.3 Additional Readings and Discussion Exercises IV Planning Methods Based on Logic 21 The Situation Calculus 21.1 Reasoning about States and Actions 21.2 Some Difficulties 21.2.1 Frame Axioms 21.2.2 Qualifications 21.2.3 Ramifications 21.3 Generating Plans 21.4 Additional Readings and Discussion Exercises H-''. 'iZZ ' 'I Planning 22.1 STRIPS Planning Systems 22.1.1 Describing States and Goals 22.1.2 Forward Search Methods 22.1.3 Recursive STRIPS 22.1.4 Plans with Run-Time Conditionals 22.1.5 The Sussman Anomaly 22.1.6 Backward Search Methods 22.2 Plan Spaces and Partial-Order Planning 22.3 Hierarchical Planning 22.3.1 ABSTRIPS 22.3.2 Combining Hierarchical and Partial-Order Planning 22.4 Learning Plans 22.5 Additional Readings and Discussion Exercises V Communication and Integration Multiple Agents 23.1 Interacting Agents 23.2 Models of Other Agents 23.2.1 Varieties of Models 23.2.2 Simulation Strategies 23.2.3 Simulated Databases 23.2.4 The Intentional Stance 23.3 A Modal Logic of Knowledge 23.3.1 Modal Operators 23.3.2 Knowledge Axioms 23.5.3 Reasoning about Other Agents' Knowledge 23.5.4 Predicting Actions of Other Agents 25.4 Additional Readings and Discussion Exercises Communication among Agents 24.1 Speech Acts 24.1.1 Planning Speech Acts 24.1.2 Implementing Speech Acts 24.2 Understanding Language Strings 24.2.1 Phrase-Structure Grammars 24.2.2 Semantic Analysis 24.2.5 Expanding the Grammar 24.3 Efficient Communication 24.3.1 Use of Context 24.3.2 Use of Knowledge to Resolve Ambiguities 24.4 Natural Language Processing 24.5 Additional Readings and Discussion Exercises Agent Architectures 25.1 Three-Level Architectures 25.2 Goal Arbitration 25.5 The Triple-Tower Architecture 25.4 Bootstrapping 25.5 Additional Readings and Discussion Exercises
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