人工智能：复杂问题求解的结构和策略 英文版PDF电子书下载

PART Ⅰ ARTIFICIAL INTELLIGENCE:ITS ROOTS AND SCOPE 1

1 AI:HISTORY AND APPLICATIONS 3

1.1 From Eden to ENIAC:Attitudes toward Intelligence,Knowledge,and Human Artifice 3

1.2 Overview of AI Application Areas 17

1.3 Artificial Intelligence—A Summary 28

1.4 Epilogue and References 29

1.5 Exercises 31

PART Ⅱ ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH 33

2.1 The Propositional Calculus 47

2 THE PREDICATE CALCULUS 47

2.0 Introduction 47

2.2 The Predicate Calculus 52

2.3 Using Inference Rules to Produce Predicate Calculus Expressions 64

2.4 Application:A Logic-Based Financial Advisor 75

2.5 Epilogue and References 79

2.6 Exercises 79

3 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH 81

3.0 Introduction 81

3.1 Graph Theory 84

3.2 Strategies for State Space Search 93

3.3 Using the State Space to Represent Reasoning with the Predicate Calculus 107

3.4 Epilogue and References 121

3.5 Exercises 121

4 HEURISTIC SEARCH 123

4.0 Introduction 123

4.1 An Algorithm for Heuristic Search 127

4.2 Admissibility,Monotonicity,and Informedness 139

4.3 Using Heuristics in Games 144

4.4 Complexity Issues 152

4.5 Epilogue and References 156

4.6 Exercises 156

5 CONTROL AND IMPLEMENTATION OF STATE SPACE SEARCH 159

5.0 Introduction 159

5.1 Recursion-Based Search 160

5.2 Pattern-Directed Search 164

5.3 Production Systems 171

5.4 The Blackboard Architecture for Problem Solving 187

5.5 Epilogue and References 189

5.6 Exercises 190

PART Ⅲ REPRESENTATION AND INTELLIGENCE:THE AI CHALLENGE 193

6 KNOWLEDGE REPRESENTATION 197

6.0 Issues in Knowledge Representation 197

6.1 A Brief History of AI Representational Systems 198

6.2 Conceptual Graphs:A Network Language 218

6.3 Alternatives to Explicit Representation 228

6.4 Agent Based and Distributed Problem Solving 235

6.5 Epilogue and References 240

6.6 Exercises 243

7 STRONG METHOD PROBLEM SOLVING 247

7.0 Introduction 247

7.1 Overview of Expert System Technology 249

7.2 Rule-Based Expert Systems 256

7.3 Model-Based,Case Based,and Hybrid Systems 268

7.4 Planning 284

7.5 Epilogue and References 299

7.6 Exercises 301

8.0 Introduction 303

8 REASONING IN UNCERTAIN SITUATIONS 303

8.1 Logic-Based Abductive Inference 305

8.2 Abduction:Alternatives to Logic 320

8.3 The Stochastic Approach to Uncertainty 333

8.4 Epilogue and References 344

8.5 Exercises 346

PART Ⅳ MACHINE LEARNING 349

9 MACHINE LEARNING:SYMBOL-BASED 351

9.1 A Framework for Symbol-based Learning 354

9.2 Version Space Search 360

9.3 The ID3 Decision Tree Induction Algorithm 372

9.4 Inductive Bias and Learnability 381

9.5 Knowledge and Learning 386

9.6 Unsupervised Learning 397

9.7 Reinforcement Learning 406

9.8 Epilogue and References 413

9.9 Exercises 414

10.0 Introduction 417

10 MACHINE LEARNING:CONNECTIONIST 417

10.1 Foundations for Connectionist Networks 419

10.2 Perceptron Learning 422

10.3 Backpropagation Learning 431

10.4 Competitive Learning 438

10.5 Hebbian Coincidence Learning 446

10.6 Attractor Networks or"Memories" 457

10.7 Epilogue and References 467

10.8 Exercises 468

11 MACHINE LEARNING:SOCIAL AND EMERGENT 469

11.0 Social and Emergent Models of Learning 469

11.1 The Genetic Algorithm 471

11.2 Classifier Systems and Genetic Programming 481

11.3 Artificial Life and Society-Based Learning 492

11.4 Epilogue and References 503

11.5 Exercises 504

PART Ⅴ ADVANCED TOPICS FOR AI PROBLEM SOLVING 507

12 AUTOMATED REASONING 509

12.0 Introduction to Weak Methods in Theorem Proving 509

12.1 The General Problem Solver and Difference Tables 510

12.2 Resolution Theorem Proving 516

12.3 PROLOG and Automated Reasoning 537

12.4 Further Issues in Automated Reasoning 543

12.5 Epilogue and References 550

12.6 Exercises 551

13 UNDERSTANDING NATURAL LANGUAGE 553

13.0 Role of Knowledge in Language Understanding 553

13.1 Deconstructing Language:A Symbolic Analysis 556

13.7 Exercises 557

13.2 Syntax 559

13.3 Syntax and Knowledge with ATN Parsers 568

13.4 Stochastic Tools for Language Analysis 578

13.5 Natural Language Applications 585

13.6 Epilogue and References 592

PART Ⅵ LANGUAGES AND PROGRAMMING TECHNIQUES FOR ARTIFICIAL INTELLIGENCE 597

14.0 Introduction 603

9.0 Introduction 603

14 AN INTRODUCTION TO PROLOG 603

14.1 Syntax for Predicate Calculus Programming 604

14.2 Abstract Data Types(ADTs)in PROLOG 616

14.3 A Production System Example in PROLOG 620

14.4 Designing Alternative Search Strategies 625

14.5 A PROLOG Planner 630

14.6 PROLOG:Meta-Predicates,Types,and Unification 633

14.7 Meta-Interpreters in PROLOG 641

14.8 Learning Algorithms in PROLOG 656

14.9 Natural Language Processing in PROLOG 666

14.10 Epilogue and References 673

14.11 Exercises 676

15 AN INTRODUCTION TO LISP 679

15.0 Introduction 679

15.1 LISP:A Brief Overview 680

15.2 Search in LISP:A Functional Approach to the Farmer,Wolf,Goat,and Cabbage Problem 702

15.3 Higher-Order Functions and Procedural Abstraction 707

15.4 Search Strategies in LISP 711

15.5 Pattern Matching in LISP 715

15.6 A Recursive Unification Function 717

15.7 Interpreters and Embedded Languages 721

15.8 Logic Programming in LISP 723

15.9 Streams and Delayed Evaluation 732

15.15 An Expert System Shell in LISP 736

15.11 Semantic Networks and Inheritance in LISP 743

15.12 Object-Oriented Programming Using CLOS 747

15.13 Learning in LISP:The ID3 Algorithm 759

15.14 Epilogue and References 771

15.15 Exercises 772

PART Ⅶ EPILOGUE 777

16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY 779

16.0 Introduction 779

16.1 Artificial Intelligence:A Revised Definition 781

16.2 The Science of Intelligent Systems 792

16.3 AI:Current Issues and Future Directions 803

16.4 Epilogue and References 807

Bibliography 809

Author Index 837

Subject Index 843