人工智能 一种现代的方法 原书第3版 英文PDF电子书下载

Ⅰ Artificial Intelligence 1

1 Introduction 1

1.1 What Is AI？ 1

1.2 The Foundations of Artificial Intelligence 5

1.3 The History of Artificial Intelligence 16

1.4 The State of the Art 28

1.5 Summary，Bibliographical and Historical Notes，Exercises 29

2 Intelligent Agents 34

2.1 Agents and Environments 34

2.2 Good Behavior：The Concept of Rationality 36

2.3 The Nature of Environments 40

2.4 The Structure of Agents 46

2.5 Summary，Bibliographical and Historical Notes，Exercises 59

Ⅱ Problem-solving 64

3 Solving Problems by Searching 64

3.1 Problem-Solving Agents 64

3.2 Example Problems 69

3.3 Searching for Solutions 75

3.4 Uninformed Search Strategies 81

3.5 Informed（Heuristic）Search Strategies 92

3.6 Heuristic Functions 102

3.7 Summary，Bibliographical and Historical Notes，Exercises 108

4 Beyond Classical Search 120

4.1 Local Search Algorithms and Optimization Problems 120

4.2 Local Search in Continuous Spaces 129

4.3 Searching with Nondeterministic Actions 133

4.4 Searching with Partial Observations 138

4.5 Online SearchAgents and Unknown Environments 147

4.6 Summary，Bibliographical and Historical Notes，Exercises 153

5 Adversarial Search 161

5.1 Games 161

5.2 Optimal Decisions in Games 163

5.3 Alpha-Beta Pruning 167

5.4 Imperfect Real-Time Decisions 171

5.5 Stochastic Games 177

5.6 Partially Observable Games 180

5.7 State-of-the-Art Game Programs 185

5.8 Alternative Approaches 187

5.9 Summary，Bibliographical and Historical Notes，Exercises 189

6 Constraint Satisfaction Problems 202

6.1 Defining Constraint Satisfaction Problems 202

6.2 Constraint Propagation：Inference in CSPs 208

6.3 Backtracking Search for CSPs 214

6.4 Local Search for CSPs 220

6.5 The Structure of Problems 222

6.6 Summary，Bibliographical and Historical Notes，Exercises 227

Ⅲ Knowledge，reasoning，and planning 234

7 Logical Agents 234

7.1 Knowledge-Based Agents 235

7.2 The Wumpus World 236

7.3 Logic 240

7.4 Propositional Logic：A Very Simple Logic 243

7.5 Propositional Theorem Proving 249

7.6 Effective Propositional Model Checking 259

7.7 Agents Based on Propositional Logic 265

7.8 Summary，Bibliographical and Historical Notes，Exercises 274

8 First-Order Logic 285

8.1 Representation Revisited 285

8.2 Syntax and Semantics of First-Order Logic 290

8.3 Using First-Order Logic 300

8.4 Knowledge Engineering in First-Order Logic 307

8.5 Summary，Bibliographical and Historical Notes，Exercises 313

9 Inference in First-Order Logic 322

9.1 Propositional vs.First-Order Inference 322

9.2 Unification and Lifting 325

9.3 Forward Chaining 330

9.4 Backward Chaining 337

9.5 Resolution 345

9.6 Summary，Bibliographical and Historical Notes，Exercises 357

10 Classical Planning 366

10.1 Definition of Classical Planning 366

10.2 Algorithms for Planning as State-Space Search 373

10.3 Planning Graphs 379

10.4 Other Classical Planning Approaches 387

10.5 Analysis of Planning Approaches 392

10.6 Summary，Bibliographical and Historical Notes，Exercises 393

11 Planning and Acting in the Real World 401

11.1 Time，Schedules，and Resources 401

11.2 Hierarchical Planning 406

11.3 Planning and Acting in Nondeterministic Domains 415

11.4 Multiagent Planning 425

11.5 Summary，Bibliographical and Historical Notes，Exercises 430

12 Knowledge Representation 437

12.1 Ontological Engineering 437

12.2 Categories and Objects 440

12.3 Events 446

12.4 Mental Events and Mental Objects 450

12.5 Reasoning Systems for Categories 453

12.6 Reasoning with Default Information 458

12.7 The Internet Shopping World 462

12.8 Summary，Bibliographical and Historical Notes，Exercises 467

Ⅳ Uncertain knowledge and reasoning 480

13 Quantifying Uncertainty 480

13.1 Acting under Uncertainty 480

13.2 Basic Probability Notation 483

13.3 Inference Using Full Joint Distributions 490

13.4 Independence 494

13.5 Bayes’Rule and Its Use 495

13.6 The Wumpus World Revisited 499

13.7 Summary，Bibliographical and Historical Notes，Exercises 503

14 Probabilistic Reasoning 510

14.1 Representing Knowledge in an Uncertain Domain 510

14.2 The Semantics of Bayesian Networks 513

14.3 Efficient Representation of Conditional Distributions 518

14.4 Exact Inference in Bayesian Networks 522

14.5 Approximate Inference in Bayesian Networks 530

14.6 Relational and First-Order Probability Models 539

14.7 Other Approaches to Uncertain Reasoning 546

14.8 Summary，Bibliographical and Historical Notes，Exercises 551

15 Probabilistic Reasoning over Time 566

15.1 Time and Uncertainty 566

15.2 Inference in Temporal Models 570

15.3 Hidden Markov Models 578

15.4 Kalman Filters 584

15.5 Dynamic Bayesian Networks 590

15.6 Keeping Track of Many Objects 599

15.7 Summary，Bibliographical and Historical Notes，Exercises 603

16 Making Simple Decisions 610

16.1 Combining Beliefs and Desires under Uncertainty 610

16.2 The Basis of Utility Theory 611

16.3 Utility Functions 615

16.4 Multiattribute Utility Functions 622

16.5 Decision Networks 626

16.6 The Value of Information 628

16.7 Decision-Theoretic Expert Systems 633

16.8 Summary，Bibliographical and Historical Notes，Exercises 636

17 Making Complex Decisions 645

17.1 Sequential Decision Problems 645

17.2 Value Iteration 652

17.3 Policy Iteration 656

17.4 Partially Observable MDPs 658

17.5 Decisions with Multiple Agents：Game Theory 666

17.6 Mechanism Design 679

17.7 Summary，Bibliographical and Historical Notes，Exercises 684

Ⅴ Learning 693

18 Learning from Examples 693

18.1 Forms of Learning 693

18.2 Supervised Learning 695

18.3 Learning Decision Trees 697

18.4 Evaluating and Choosing the Best Hypothesis 708

18.5 The Theory of Leaming 713

18.6 Regression and Classification with Linear Models 717

18.7 Artificial Neural Networks 727

18.8 Nonparametric Models 737

18.9 Support Vector Machines 744

18.10 Ensemble Learning 748

18.11 Practical Machine Learning 753

18.12 Summary，Bibliographical and Historical Notes，Exercises 757

19 Knowledge in Learning 768

19.1 A Logical Formulation of Learning 768

19.2 Knowledge in Learning 777

19.3 Explanation-Based Learning 780

19.4 Learning Using Relevance Information 784

19.5 Inductive Logic Programming 788

19.6 Summary，Bibliographical and Historical Notes，Exercises 797

20 Learning Probabilistic Models 802

20.1 Statistical Learning 802

20.2 Learning with Complete Data 806

20.3 Learning with Hidden Variables：The EM Algorithm 816

20.4 Summary，Bibliographical and Historical Notes，Exercises 825

21 Reinforcement Learning 830

21.1 Introduction 830

21.2 Passive Reinforcement Learning 832

21.3 Active Reinforcement Learning 839

21.4 Generalization in Reinforcement Learning 845

21.5 Policy Search 848

21.6 Applications of Reinforcement Learning 850

21.7 Summary，Bibliographical and Historical Notes，Exercises 853

Ⅵ Communicating，perceiving，and acting 860

22 Natural Language Processing 860

22.1 Language Models 860

22.2 Text Classification 865

22.3 Information Retrieval 867

22.4 Information Extraction 873

22.5 Summary，Bibliographical and Historical Notes，Exercises 882

23 Natural Language for Communication 888

23.1 Phrase Structure Grammars 888

23.2 Syntactic Analysis（Parsing） 892

23.3 Augmented Grammars and Semantic Interpretation 897

23.4 Machine Translation 907

23.5 Speech Recognition 912

23.6 Summary，Bibliographical and Historical Notes，Exercises 918

24 Perception 928

24.1 Image Formation 929

24.2 Early Image-Processing Operations 935

24.3 Object Recognition by Appearance 942

24.4 Reconstructing the 3D World 947

24.5 Object Recognition from Structural Information 957

24.6 Using Vision 961

24.7 Summary，Bibliographical and Historical Notes，Exercises 965

25 Robotics 971

25.1 Introduction 971

25.2 Robot Hardware 973

25.3 Robotic Perception 978

25.4 Planning to Move 986

25.5 Planning Uncertain Movements 993

25.6 Moving 997

25.7 Robotic Software Architectures 1003

25.8 Application Domains 1006

25.9 Summary，Bibliographical and Historical Notes，Exercises 1010

Ⅶ Conclusions 1020

26 Philosophical Foundations 1020

26.1 Weak AI：Can Machines Act Intelligently？ 1020

26.2 Strong AI：Can Machines Really Think？ 1026

26.3 The Ethics and Risks of Developing Artificial Intelligence 1034

26.4 Summary，Bibliographical and Historical Notes，Exercises 1040

27 AI：ThePresentandFuture 1044

27.1 Agent Components 1044

27.2 Agent Architectures 1047

27.3 Are We Going in the Right Direction？ 1049

27.4 What If AI Does Succeed？ 1051

A Mathematical background 1053

A.1 Complexity Analysis and O（）Notation 1053

A.2 Vectors，Matrices，and Linear Algebra 1055

A.3 Probability Distributions 1057

B Notes on Languages and Algorithms 1060

B.1 Defining Languages with Backus-Naur Form（BNF） 1060

B.2 Describing Algorithms with Pseudocode 1061

B.3 Online Help 1062

Bibliography 1063

Index 1095