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Ⅰ Artificial Intelligence 1

1 Introduction 1

1.1 What is AI? 1

Acting humanly: The Turing Test approach 2

Thinking humanly: The cognitive modeling approach 3

Thinking rationally: The “laws of thought” approach 4

Acting rationally: The rational agent approach 4

1.2 The Foundations of Artificial Intelligence 5

Philosophy （428 B.C.-present） 5

Mathematics （c.800-present） 7

Economics （1776-present） 9

Neuroscience （1861-present） 10

Psychology （1879-present） 12

Computer engineering （1940-present） 14

Control theory and Cybernetics （1948-present） 15

Linguistics （1957-present） 16

1.3 The History of Artificial Intelligence 16

The gestation of artificial intelligence （1943-1955） 16

The birth of artificial intelligence （1956） 17

Early enthusiasm, great expectations （1952-1969） 18

A dose of reality （1966-1973） 21

Knowledge-based systems: The key to power? （1969-1979） 22

Al becomes an industry （1980-present） 24

The return of neural networks （1986-present） 25

AI becomes a science （1987-present） 25

The emergence of intelligent agents （1995-present） 27

1.4 The State of the Art 27

1.5 Summary 28

Bibliographical and Historical Notes 29

Exercises 30

2 Intelligent Agents 32

2.1 Agents and Environments 32

2.2 Good Behavior: The Concept of Rationality 34

Performance measures 35

Rationality 35

Omniscience, learning, and autonomy 36

2.3 The Nature of Environments 38

Specifying the task environment 38

Properties of task environments 40

2.4 The Structure of Agents 44

Agent programs 44

Simple reflex agents 46

Model-based reflex agents 48

Goal-based agents 49

Utility-based agents 51

Learning agents 51

2.5 Summary 54

Bibliographical and Historical Notes 55

Exercises 56

Ⅱ Problem-solving 59

3 Solving Problems by Searching 59

3.1 Problem-Solving Agents 59

Well-defined problems and solutions 62

Formulating problems 62

3.2 Example Problems 64

Toy problems 64

Real-world problems 67

3.3 Searching for Solutions 69

Measuring problem-solving performance 71

3.4 Uninformed Search Strategies 73

Breadth-first search 73

Depth-first search 75

Depth-limited search 77

Iterative deepening depth-first search 78

Bidirectional search 79

Comparing uninformed search strategies 81

3.5 Avoiding Repeated States 81

3.6 Searching with Partial Information 83

Sensorless problems 84

Contingency problems 86

3.7 Summary 87

Bibliographical and Historical Notes 88

Exercises 89

4 Informed Search and Exploration 94

4.1 Informed （Heuristic） Search Strategies 94

Greedy best-first search 95

A search: Minimizing the total estimated solution cost 97

Memory-bounded heuristic search 101

Learning to search better 104

4.2 Heuristic Functions 105

The effect of heuristic accuracy on performance 106

Inventing admissible heuristic functions 107

Learning heuristics from experience 109

4.3 Local Search Algorithms and Optimization Problems 110

Hill-climbing search 111

Simulated annealing search 115

Local beam search 115

Genetic algorithms 116

4.4 Local Search in Continuous Spaces 119

4.5 Online Search Agents and Unknown Environments 122

Online search problems 123

Online search agents 125

Online local search 126

Learning in online search 127

4.6 Summary 129

Bibliographical and Historical Notes 130

Exercises 134

5 Constraint Satisfaction Problems 137

5.1 Constraint Satisfaction Problems 137

5.2 Backtracking Search for CSPs 141

Variable and value ordering 143

Propagating information through constraints 144

Intelligent backtracking: looking backward 148

5.3 Local Search for Constraint Satisfaction Problems 150

5.4 The Structure of Problems 151

5.5 Summary 155

Bibliographical and Historical Notes 156

Exercises 158

6 Adversarial Search 161

6.1 Games 161

6.2 Optimal Decisions in Games 162

Optimal strategies 163

The minimax algorithm 165

Optimal decisions in multiplayer games 165

6.3 Alpha-Beta Pruning 167

6.4 Imperfect, Real-Time Decisions 171

Evaluation functions 171

Cutting off search 173

6.5 Games That Include an Element of Chance 175

Position evaluation in games with chance nodes 177

Complexity of expectiminimax 177

Card games 179

6.6 State-of-the-Art Game Programs 180

6.7 Discussion 183

6.8 Summary 185

Bibliographical and Historical Notes 186

Exercises 189

Ⅲ Knowledge and reasoning 194

7 Logical Agents 194

7.1 Knowledge-Based Agents 195

7.2 The Wumpus World 197

7.3 Logic 200

7.4 Propositional Logic: A Very Simple Logic 204

Syntax 204

Semantics 206

A simple knowledge base 208

Inference 208

Equivalence, validity, and satisfiability 210

7.5 Reasoning Patterns in Propositional Logic 211

Resolution 213

Forward and backward chaining 217

7.6 Effective propositional inference 220

A complete backtracking algorithm 221

Local-search algorithms 222

Hard satisfiability problems 224

7.7 Agents Based on Propositional Logic 225

Finding pits and wumpuses using logical inference 225

Keeping track of location and orientation 227

Circuit-based agents 227

A comparison 231

7.8 Summary 232

Bibliographical and Historical Notes 233

Exercises 236

8 First-Order Logic 240

8.1 Representation Revisited 240

8.2 Syntax and Semantics of First-Order Logic 245

Models for first-order logic 245

Symbols and interpretations 246

Terms 248

Atomic sentences 248

Complex sentences 249

Quantifiers 249

Equality 253

8.3 Using First-Order Logic 253

Assertions and queries in first-order logic 253

The kinship domain 254

Numbers, sets, and lists 256

The wumpus world 258

8.4 Knowledge Engineering in First-Order Logic 260

The knowledge engineering process 261

The electronic circuits domain 262

8.5 Summary 266

Bibliographical and Historical Notes 267

Exercises 268

9 Inference in First-Order Logic 272

9.1 Propositional vs.First-Order Inference 272

Inference rules for quantifiers 273

Reduction to propositional inference 274

9.2 Unification and Lifting 275

A first-order inference rule 275

Unification 276

Storage and retrieval 278

9.3 Forward Chaining 280

First-order definite clauses 280

A simple forward-chaining algorithm 281

Efficient forward chaining 283

9.4 Backward Chaining 287

A backward chaining algorithm 287

Logic programming 289

Efficient implementation of logic programs 290

Redundant inference and infinite loops 292

Constraint logic programming 294

9.5 Resolution 295

Conjunctive normal form for first-order logic 295

The resolution inference rule 297

Example proofs 297

Completeness of resolution 300

Dealing with equality 303

Resolution strategies 304

Theorem provers 306

9.6 Summary 310

Bibliographical and Historical Notes 310

Exercises 315

10 Knowledge Representation 320

10.1 Ontological Engineering 320

10.2 Categories and Objects 322

Physical composition 324

Measurements 325

Substances and objects 327

10.3 Actions, Situations, and Events 328

The ontology of situation calculus 329

Describing actions in situation calculus 330

Solving the representational frame problem 332

Solving the inferential frame problem 333

Time and event calculus 334

Generalized events 335

Processes 337

Intervals 338

Fluents and objects 339

10.4 Mental Events and Mental Objects 341

A formal theory of beliefs 341

Knowledge and belief 343

Knowledge, time, and action 344

10.5 The Internet Shopping World 344

Comparing offers 348

10.6 Reasoning Systems for Categories 349

Semantic networks 350

Description logics 353

10.7 Reasoning with Default Information 354

Open and closed worlds 354

Negation as failure and stable model semantics 356

Circumscription and default logic 358

10.8 Truth Maintenance Systems 360

10.9 Summary 362

Bibliographical and Historical Notes 363

Exercises 369

Ⅳ Planning 375

11 Planning 375

11.1 The Planning Problem 375

The language of planning problems 377

Expressiveness and extensions 378

Example: Air cargo transport 380

Example: The spare tire problem 381

Example: The blocks world 381

11.2 Planning with State-Space Search 382

Forward state-space search 382

Backward state-space search 384

Heuristics for state-space search 386

11.3 Partial-Order Planning 387

A partial-order planning example 391

Partial-order planning with unbound variables 393

Heuristics for partial-order planning 394

11.4 Planning Graphs 395

Planning graphs for heuristic estimation 397

The GRAPHPLAN algorithm 398

Termination of GRAPHPLAN 401

11.5 Planning with Propositional Logic 402

Describing planning problems in propositional logic 402

Complexity of propositional encodings 405

11.6 Analysis of Planning Approaches 407

11.7 Summary 408

Bibliographical and Historical Notes 409

Exercises 412

12 Planning and Acting in the Real World 417

12.1 Time, Schedules, and Resources 417

Scheduling with resource constraints 420

12.2 Hierarchical Task Network Planning 422

Representing action decompositions 423

Modifying the planner for decompositions 425

Discussion 427

12.3 Planning and Acting in Nondeterministic Domains 430

12.4 Conditional Planning 433

Conditional planning in fully observable environments 433

Conditional planning in partially observable environments 437

12.5 Execution Monitoring and Replanning 441

12.6 Continuous Planning 445

12.7 MultiAgent Planning 449

Cooperation: Joint goals and plans 450

Multibody planning 451

Coordination mechanisms 452

Competition 454

12.8 Summary 454

Bibliographical and Historical Notes 455

Exercises 459

Ⅴ Uncertain knowledge and reasoning 462

13 Uncertainty 462

13.1 Acting under Uncertainty 462

Handling uncertain knowledge 463

Uncertainty and rational decisions 465

Design for a decision-theoretic agent 466

13.2 Basic Probability Notation 466

Propositions 467

Atomic events 468

Prior probability 468

Conditional probability 470

13.3 The Axioms of Probability 471

Using the axioms of probability 473

Why the axioms of probability are reasonable 473

13.4 Inference Using Full Joint Distributions 475

13.5 Independence 477

13.6 Bayes' Rule and Its Use 479

Applying Bayes' rule: The simple case 480

Using Bayes' rule: Combining evidence 481

13.7 The Wumpus World Revisited 483

13.8 Summary 486

Bibliographical and Historical Notes 487

Exercises 489

14 Probabilistic Reasoning 492

14.1 Representing Knowledge in an Uncertain Domain 492

14.2 The Semantics of Bayesian Networks 495

Representing the full joint distribution 495

Conditional independence relations in Bayesian networks 499

14.3 Efficient Representation of Conditional Distributions 500

14.4 Exact Inference in Bayesian Networks 504

Inference by enumeration 504

The variable elimination algorithm 507

The complexity of exact inference 509

Clustering algorithms 510

14.5 Approximate Inference in Bayesian Networks 511

Direct sampling methods 511

Inference by Markov chain simulation 516

14.6 Extending Probability to First-Order Representations 519

14.7 Other Approaches to Uncertain Reasoning 523

Rule-based methods for uncertain reasoning 524

Representing ignorance: Dempster-Shafer theory 525

Representing vagueness: Fuzzy sets and fuzzy logic 526

14.8 Summary 528

Bibliographical and Historical Notes 528

Exercises 533

15 Probabilistic Reasoning over Time 537

15.1 Time and Uncertainty 537

States and observations 538

Stationary processes and the Markov assumption 538

15.2 Inference in Temporal Models 541

Filtering and prediction 542

Smoothing 544

Finding the most likely sequence 547

15.3 Hidden Markov Models 549

Simplified matrix algorithms 549

15.4 Kalman Filters 551

Updating Gaussian distributions 553

A simple one-dimensional example 554

The general case 556

Applicability of Kalman filtering 557

15.5 Dynamic Bayesian Networks 559

Constructing DBNs 560

Exact inference in DBNs 563

Approximate inference in DBNs 565

15.6 Speech Recognition 568

Speech sounds 570

Words 572

Sentences 574

Building a speech recognizer 576

15.7 Summary 578

Bibliographical and Historical Notes 578

Exercises 581

16 Making Simple Decisions 584

16.1 Combining Beliefs and Desires under Uncertainty 584

16.2 The Basis of Utility Theory 586

Constraints on rational preferences 586

And then there was Utility 588

16.3 Utility Functions 589

The utility of money 589

Utility scales and utility assessment 591

16.4 Multiattribute Utility Functions 593

Dominance 594

Preference structure and multiattribute utility 596

16.5 Decision Networks 597

Representing a decision problem with a decision network 598

Evaluating decision networks 599

16.6 The Value of Information 600

A simple example 600

A general formula 601

Properties of the value of information 602

Implementing an information-gathering agent 603

16.7 Decision-Theoretic Expert Systems 604

16.8 Summary 607

Bibliographical and Historical Notes 607

Exercises 609

17 Making Complex Decisions 613

17.1 Sequential Decision Problems 613

An example 613

Optimality in sequential decision problems 616

17.2 Value Iteration 618

Utilities of states 619

The value iteration algorithm 620

Convergence of value iteration 620

17.3 Policy Iteration 624

17.4 Partially observable MDPs 625

17.5 Decision-Theoretic Agents 629

17.6 Decisions with Multiple Agents: Game Theory 631

17.7 Mechanism Design 640

17.8 Summary 643

Bibliographical and Historical Notes 644

Exercises 646

Ⅵ Learning 649

18 Learning from Observations 649

18.1 Forms of Learning 649

18.2 Inductive Learning 651

18.3 Learning Decision Trees 653

Decision trees as performance elements 653

Expressiveness of decision trees 655

Inducing decision trees from examples 655

Choosing attribute tests 659

Assessing the performance of the learning algorithm 660

Noise and overfitting 661

Broadening the applicability of decision trees 663

18.4 Ensemble Learning 664

18.5 Why Learning Works: Computational Learning Theory 668

How many examples are needed? 669

Learning decision lists 670

Discussion 672

18.6 Summary 673

Bibliographical and Historical Notes 674

Exercises 676

19 Knowledge in Learning 678

19.1 A Logical Formulation of Learning 678

Examples and hypotheses 678

Current-best-hypothesis search 680

Least-commitment search 683

19.2 Knowledge in Learning 686

Some simple examples 687

Some general schemes 688

19.3 Explanation-Based Learning 690

Extracting general rules from examples 691

Improving efficiency 693

19.4 Learning Using Relevance Information 694

Determining the hypothesis space 695

Learning and using relevance information 695

19.5 Inductive Logic Programming 697

An example 699

Top-down inductive learning methods 701

Inductive learning with inverse deduction 703

Making discoveries with inductive logic programming 705

19.6 Summary 707

Bibliographical and Historical Notes 708

Exercises 710

20 Statistical Learning Methods 712

20.1 Statistical Learning 712

20.2 Learning with Complete Data 716

Maximum-likelihood parameter learning: Discrete models 716

Naive Bayes models 718

Maximum-likelihood parameter learning: Continuous models 719

Bayesian parameter learning 720

Learning Bayes net structures 722

20.3 Lerning with Hidden Variables: The EM Algorithm 724

Unsupervised clustering: Learning mixtures of Gaussians 725

Learning Bayesian networks with hidden variables 727

Learning hidden Markov models 731

The general form of the EM algorithm 731

Learning Bayes net structures with hidden variables 732

20.4 Instance-Based Learning 733

Nearest-neighbor models 733

Kernel models 735

20.5 Neural Networks 736

Units in neural networks 737

Network structures 738

Single layer feed-forward neural networks （perceptrons） 740

Multilayer feed-forward neural networks 744

Learning neural network structures 748

20.6 Kernel Machines 749

20.7 Case Study: Handwritten Digit Recognition 752

20.8 Summary 754

Bibliographical and Historical Notes 755

Exercises 759

21 Reinforcement Learning 763

21.1 Introduction 763

21.2 Passive Reinforcement Learning 765

Direct utility estimation 766

Adaptive dynamic programming 767

Temporal difference learning 767

21.3 Active Reinforcement Learning 771

Exploration 771

Learning an Action-Value Function 775

21.4 Generalization in Reinforcement Learning 777

Applications to game-playing 780

Application to robot control 780

21.5 Policy Search 781

21.6 Summary 784

Bibliographical and Historical Notes 785

Exercises 788

Ⅶ Communicating, perceiving, and acting 790

22 Communication 790

22.1 Communication as Action 790

Fundamentals of language 791

The component steps of communication 792

22.2 A Formal Grammar for a Fragment of English 795

The Lexicon of ε0 795

The Grammar of ε0 796

22.3 Syntactic Analysis （Parsing） 798

Efficient parsing 800

22.4 Augmented Grammars 806

Verb subcategorization 808

Generative capacity of augmented grammars 809

22.5 Semantic Interpretation 810

The semantics of an English fragment 811

Time and tense 812

Quantification 813

Pragmatic Interpretation 815

Language generation with DCGs 817

22.6 Ambiguity and Disambiguation 818

Disambiguation 820

22.7 Discourse Understanding 821

Reference resolution 821

The structure of coherent discourse 823

22.8 Grammar Induction 824

22.9 Summary 826

Bibliographical and Historical Notes 827

Exercises 831

23 Probabilistic Language Processing 834

23.1 Probabilistic Language Models 834

Probabilistic context-free grammars 836

Learning probabilities for PCFGs 839

Learning rule structure for PCFGs 840

23.2 Information Retrieval 840

Evaluating IR systems 842

IR refinements 844

Presentation of result sets 845

Implementing IR systems 846

23.3 Information Extraction 848

23.4 Machine Translation 850

Machine translation systems 852

Statistical machine translation 853

Learning probabilities for machine translation 856

23.5 Summary 857

Bibliographical and Historical Notes 858

Exercises 861

24 Perception 863

24.1 Introduction 863

24.2 Image Formation 865

Images without lenses: the pinhole camera 865

Lens systems 866

Light: the photometry of image formation 867

Color: the spectrophotometry of image formation 868

24.3 Early Image Processing Operations 869

Edge detection 870

Image segmentation 872

24.4 Extracting Three-Dimensional Information 873

Motion 875

Binocular stereopsis 876

Texture gradients 879

Shading 880

Contour 881

24.5 Object Recognition 885

Brightness-based recognition 887

Feature-based recognition 888

Pose Estimation 890

24.6 Using Vision for Manipulation and Navigation 892

24.7 Summary 894

Bibliographical and Historical Notes 895

Exercises 898

25 Robotics 901

25.1 Introduction 901

25.2 Robot Hardware 903

Sensors 903

Effectors 904

25.3 Robotic Perception 907

Localization 908

Mapping 913

Other types of perception 915

25.4 Planning to Move 916

Configuration space 916

Cell decomposition methods 919

Skeletonization methods 922

25.5 Planning uncertain movements 923

Robust methods 924

25.6 Moving 926

Dynamics and control 927

Potential field control 929

Reactive control 930

25.7 Robotic Software Architectures 932

Subsumption architecture 932

Three-layer architecture 933

Robotic programming languages 934

25.8 Application Domains 935

25.9 Summary 938

Bibliographical and Historical Notes 939

Exercises 942

Ⅷ Conclusions 947

26 Philosophical Foundations 947

26.1 Weak AI: Can Machines Act Intelligently? 947

The argument from disability 948

The mathematical objection 949

The argument from informality 950

26.2 Strong AI: Can Machines Really Think? 952

The mind-body problem 954

The “brain in a vat” experiment 955

The brain prosthesis experiment 956

The Chinese room 958

26.3 The Ethics and Risks of Developing Artificial Intelligence 960

26.4 Summary 964

Bibliographical and Historical Notes 964

Exercises 967

27 AI: Present and Future 968

27.1 Agent Components 968

27.2 Agent Architectures 970

27.3 Are We Going in the Right Direction? 972

27.4 What if AI Does Succeed? 974

A Mathematical background 977

A.1 Complexity Analysis and O（） Notation 977

Asymptotic analysis 977

NP and inherently hard problems 978

A.2 Vectors, Matrices, and Linear Algebra 979

A.3 Probability Distributions 981

Bibliographical and Historical Notes 983

B Notes on Languages and Algorithms 984

B.1 Defining Languages with Backus-Naur Form （BNF） 984

B.2 Describing Algorithms with Pseudocode 985

B.3 Online Help 985

Bibliography 987

Index 1045