Ⅰ Artificial Intelligence 1
1 Introduction 3
1.1 What Is AI? 4
·Acting humanly: The Turing Test approach 5
·Thinking humanly: The cognitive modelling approach 6
·Thinking rationally: The laws of thought approach 6
·Acting rationally: The rational agent approach 7
1.2 The Foundations of Artificial Intelligence 8
·Philosophy(428 B.C.-present) 8
·Mathematics(c.800-present) 11
·Psychology (1879-present) 12
·Computer engineering(1940-present) 14
·Linguistics(1957-present) 15
1.3 The History of Artificial Intelligence 16
·The gestation of artificial Intelligence(1943-1956) 16
·Early enthusiasm, great expectations(1952-1969) 17
·A dose of reality(1966-1974) 20
·Knowledge-based systems: The key to power?(1969-1979) 22
·AI becomes an Industry(1980-1988) 24
·The return of neural networks(1986-present) 24
·Recent events(1987-present) 25
1.4 The State of the Art 26
1.5 Summary 27
Bibliographical and Historical Notes 28
Exercises 28
2 Intelligent Agents 31
2.1 Introduction 31
2.2 How Agents Should Act 31
·The Ideal mapping from percept sequences to actions 34
·Autonomy 35
2.3 Structure of Intelligent Agents 35
·Agent programs 37
·Why not just look up the answers? 38
·An example 39
·Simple reflex agents 40
·Agents that keep track of the world 41
·Goal-based agents 42
·Utility-based agents 44
2.4 Environments 45
·Properties of environments 46
·Environment programs 47
2.5 Summary 49
·Bibliographical and Historical Notes 50
·Exercises 50
Ⅱ Problem-solving 53
3 Solving Problems by Searching 55
3.1 Problem-Solving Agents 55
3.2 Formulating Problems 57
·Knowledge and problem types 58
·Well-defined problems and solutions 60
·Measuring problem-solving performance 61
·Choosing states and actions 61
3.3 Example Problems 63
·Toy Problems 63
·Real-world problems 68
3.4 Searching for Solutions 70
·Generating action sequences 70
·Data structures for search trees 72
3.5 Search Strategies 73
·Breadth-first search 74
·Uniform cost search 75
·Depth-first search 77
·Depth-limited search 78
·Iterative deepening search 78
·Bidirectional search 80
·Comparing search strategies 81
3.6 Avoiding Repeated States 82
3.7 Constraint Satisfaction Search 83
3.8 Summary 85
Bibliographical and Historical Notes 86
Exercises 87
4 Informed Search Methods 92
4.1 Best-First Search 92
·Minimize estimated cost to reach a goal:Greedy Search 93
·Minimizing the total path cost: A* search 96
4.2 Heuristic Functions 101
·The effect of heuristic accuracy on performance 102
·Inventing heuristic functions 103
·Heuristics for constraint satisfaction problems 104
4.3 Memory bounded Search 106
·Iterative deepening A* search (IDA*) 106
·SMA* search 107
4.4 Iterative Improvement Algorithms 111
·Hill-climbing search 111
·Simulated annealing 113
·Applications in constraint Satisfaction problems 114
4.5 Summary 115
Bibliographical and Historical Notes 115
Exercises 118
5 Game Playing 122
5.1 Introduction: games as Search Problems 122
5.2 Perfect Decisions in Two-Person Games 123
5.3 Imperfect Decisions 126
·Evaluation functions 127
·Cutting off search 129
5.4 Alpha-Beta Pruning 129
·Effectiveness of alpha-beta pruning 131
5.5 Games That Include an Element of Chance 133
·Position evaluation in games with chance nodes 135
·Complexity of expectiminimax 135
5.6 State-of-the-Art Game Programs 136
·Chess 137
·Checkers or Draughts 138
·Othello 138
·Backgammon 139
·Go 139
5.7 Discussion 139
5.8 Summary 141
Bibliographical and Historical Notes 141
Exercises 145
Ⅲ Knowledge and reasoning 149
6 Agents that Reason Logically 151
6.1 A Knowledge-Based Agent 151
6.2 The Wumpus World Environment 153
·Specifying the environment 154
·Acting and reasoning in the wumpus world 155
6.3 Representation, Reasoning, and Logic 157
·Representation 160
·Inference 163
·Logics 165
6.4 Propositional Logic: A Very Simple Logic 166
·Syntax 166
·Semantics 168
·Validity and inference 169
·Models 170
·Rules of inference for propositional logic 171
·Complexity of propositional inference 173
6.5 An Agent for the Wumpus World 174
·The knowledge base 174
·Finding the wumpus 175
·Translating knowledge into action 176
·Problems with the propositional agent 176
6.6 Summary 178
Bibliographical and Historical Notes 178
Exercises 180
7 First-Order Logic 185
7.1 Syntax and Semantics 186
·Terms 188
·Atomic sentences 189
·Complex sentences 189
·Quantifiers 189
·Equality 193
7.2 Extensions and Notational Variations 194
·Higher-order logic 195
·Functional and predicate expressions using the λ operator 195
·The uniqueness quantifier Э! 196
·The uniqueness operator ι 196
·Notational variations 196
7.3 Using First-Order Logic 197
·The kinship domain 197
·Axioms, definitions, and theorems 198
·The domain of sets 199
·Special notations for sets, lists and arithmetic 200
·Asking questions and getting answers 200
7.4 Logical Agents for the Wumpus World 201
7.5 A Simple Reflex Agent 202
Limitations of simple reflex agents 203
7.6 Representing Change in the World 203
·Situation calculus 204
·Keeping track of location 206
7.7 Deducing Hidden Properties of the World 208
7.8 Preferences Among Actions 210
7.9 Toward a Goal-Based Agent 211
7.10 Summary 211
Bibliographical and Historical Notes 212
Exercises 213
8 Building a knowledge Base 217
8.1 Properties of Good and Bad Knowledge Bases 218
8.2 Knowledge Engineering 221
8.3 The Electronic Circuits Domain 223
·Decide what to talk about 223
·Decide on a vocabulary 224
·Encode general rules 225
·Encode the specific instance 225
·Pose queries to the inference procedure 226
8.4 General Ontology 226
·Representing Categories 229
·Measures 231
·Composite objects 233
·Representing change with events 234
·Times, intervals, and actions 238
·Objects revisited 240
·Substances and objects 241
·Mental events and mental objects 243
·Knowledge and action 247
8.5 The Grocery Shopping World 247
·Complete description of the shopping simulation 248
·Organizing knowledge 249
·Menu-planning 249
·Navigating 252
·Gathering 253
·Communicating 254
·Paying 256
8.6 Summary 256
Bibliographical and Historical Notes 256
Exercises 261
9 Inference in First-Order Logic 265
9.1 Inference Rules Involving Quantifiers 265
9.2 An Example Proof 266
9.3 Generalized Modus Ponens 269
·Canonical form 270
·Unification 270
·Sample proof revisited 271
9.4 Forward and Backward Chaining 272
·Forward-chaining algorithm 273
·Backward-chaining algorithm 275
9.5 Completeness 276
9.6 Resolution: A Complete Inference Procedure 277
·The resolution inference rule 278
·Canonical forms for resolution 278
·Resolution Proofs 279
·Conversion to Normal Form 281
·Example proof 282
·Dealing with equality 284
·Resolution strategies 284
9.7 Completeness of resolution 286
9.8 Summary 290
Bibliographical and Historical Notes 291
Exercises 294
10 Logical Reasoning Systens 297
10.1 Introduction 297
10.2 Indexing, Retrieval, and Unification 299
·Implementing sentences and terms 299
·Store and fetch 299
·Table-based indexing 300
·Tree-based indexing 301
·The unification algorithm 302
10.3 Logic Programming Systems 304
·The Prolog language 304
·Implementation 305
·Compilation of logic programs 306
·Other logic programming languages 308
·Advanced control facilities 308
10.4 Theorem Provers 310
·Design of a theorem prover 310
·Extending Prolog 311
·Theorem provers as assistants 312
·Practical uses of theorem provers 313
10.5 Forward-Chaining Production Systems 313
·Match phase 314
·Conflict resolution phase 315
·Practical uses of production systems 316
10.6 Frame Systems and Semantic Networks 316
·Syntax and semantics of semantic networks 317
·Inheritance with exceptions 319
·Multiple inheritance 320
·Inheritance and change 320
·Implementation of semantic networks 321
·Expressiveness of semantic networks 323
10.7 Description Logics 323
·Practical uses of description logics 325
10.8 Managing Retractions, Assumptions, and Explanations 325
10.9 Summary 327
Bibliographical and Historical Notes 328
Exercises 332
Ⅳ Acting logically 335
11 Planning 337
11.1 A Simple Planning Agent 337
11.2 From Problem Solving to Planning 3380
11.3 Planning in Situation Calculus 341
11.4 Basic Representations for Planning 343
·Representations for states and goals 343
·Representations for actions 344
·Situation space and plan space 345
·Representations for plans 346
·Solutions 349
11.5 A Partial-Order Planning Example 349
11.6 A Partial-Order Planning Algorithm 355
11.7 Planning with Partially Instantiated Operators 357
11.8 Knowledge Engineering for Planning 359
·The blocks world 359
·Shakey's world 360
11.9 Summary 362
Bibliographical and Historical Notes 363
Exercises 364
12 Practical Planning 367
12.1 Practical Planners 367
·Spacecraft assembly, integration, and verification 367
·Job shop scheduling 369
·Scheduling for space missions 369
·Buildings, aircraft carriers, and beer factories 371
12.2 Hierarchical Decomposition 371
·Extending the language 372
·Modifying the planner 374
12.3 Analysis of Hierarchical Decomposition 375
·Decomposition and sharing 379
·Decomposition versus approximation 380
12.4 More Expressive Operator Descriptions 381
·Conditional effects 381
·Negated and disjunctive goals 382
·Universal quantification 383
·A planner for expressive operator descriptions 384
12.5 Resource Constraints 386
·Using measures in planning 386
·Temporal constraints 388
12.6 Summary 388
Bibliographical and Historical Notes 389
Exercises 390
13 Planning and Acting 392
13.1 Conditional Planning 393
·The nature of conditional plans 393
·An algorithm for generating conditional plans 395
·Extending the plan language 398
13.2 A Simple Replanning Agent 401
·Simple replanning with execution monitoring 402
13.3 Fully Integrated Planning and Execution 403
13.4 Discussion and Extensions 407
·Comparing conditional planning and replanning 407
·Coercion and abstraction 409
13.5 Summary 410
Bibliographical and Historical Notes 411
Exercises 412
Ⅴ Uncertain knowledge and reasoning 413
14 Uncertainty 415
14.1 Acting under Uncertainty 415
·Handing uncertain knowledge 416
·Uncertainty and rational decisions 418
·Design for a decision-theoretic agent 419
14.2 Basic Probability Notation 420
·Prior probability 420
·Conditional probability 421
14.3 The Axioms of Probability 422
·Why the axioms of probability are reasonable 423
·The joint probability distribution 425
14.4 Bayes' Rule and Its Use 426
·Applying Bayes' rule: The simple case 426
·Normalization 427
·Using Bayes' rule: Combining evidence 428
14.5 Where Do Probabilities Come From? 430
14.6 Summary 431
Bibliographical and Historical Notes 431
Exercises 433
15 Probabilistic Reasoning Systems 436
15.1 Representing Knowledge in an Uncertain Domain 436
15.2 The Semantics of Belief Networks 438
·Representing the joint probability distribution 439
·Conditional independence relations in belief networks 444
15.3 Inference in Belief Networks 445
·The nature of probabilistic inferences 446
·An algorithm for answering queries 447
15.4 Inference in Multiply Connected Belief Networks 453
·Clustering methods 453
·Cutset conditioning methods 454
·Stochastic simulation methods 455
15.5 Knowledge Engineering for Uncertain Reasoning 456
·Case study: The Pathfinder system 457
15.6 Other Approaches to Uncertain Reasoning 458
·Defaulet reasoning 459
·Rule-based methods for uncertain reasoning 460
·Representing ignorance: Dempster-Shafer theory 462
·Representing vagueness: Fuzzy sets and fuzzy logic 463
15.7 Summary 464
Bibliographical and Historical Notes 464
Exercises 467
16 Making Simple Decisions 471
16.1 Combining Beliefs and Desires Under Uncertainty 471
16.2 The Basis of Utility Theory 473
·Constraints on rational preferences 473
·…and then there was Utility 474
16.3 Utility Functions 475
·The utility of money 476
·Utility Scales and utility assessment 478
16.4 Multiattribute utility functions 480
·Dominance 481
·Preference structure and multiattribute utility 483
16.5 Decision Networks 484
·Representing a decision problem using decision net works 484
·Evaluating decision networks 486
16.6 The Value of Information 487
·A simple example 487
·A general formula 488
·Properties of the value of information 489
·Implementing an information-gathering agent 490
16.7 Decision-Theoretic Expert Systems 491
16.8 Summary 493
Bibliographical and Historical Notes 493
Exercises 495
17 Making Complex Decision Problems 498
17.1 Sequential Decision Problems 498
17.2 Value Iteration 502
17.3 Policy Iteration 505
17.4 Decision-Theoretic Agent Design 508
·The decision cycle of a rational agent 508
·Sensing in uncertain worlds 510
17.5 Dynamic Belief Networks 514
17.6 Dynamic Decision Networks 516
·Discussion 518
17.7 Summary 519
Bibliographical and Historical Notes 520
Exercises 521
Ⅵ Learning 523
18 Learning from Observations 525
18.1 A General Model of Learning Agents 525
·Components of the performance element 527
·Representation of the components 528
·Available feedback 528
·Prior knowledge 528
·Bringing it all together 529
18.2 Inductive Learning 529
18.3 Learning Decision Trees 531
·Decision trees as performance elements 531
·Expressiveness of decision trees 532
·Inducing decision trees from examples 534
·Assessing the performance of the learning algorithm 538
·Practical uses of decision tree learning 538
18.4 Using Information Theory 540
·Noise and overfitting 542
·Broadening the applicability of decision trees 543
18.5 Learning General Logical Descriptions 544
·Hypotheses 544
·Examples 545
·Current-best-hypothesis search 546
·Least-commitment search 549
·Discussion 552
18.6 Why Learning Works: Computational Learning Theory 552
·How many examples are needed? 553
·Learning decision lists 555
·Discussion 557
18.7 Summary 558
Bibliographical and Historical Notes 559
Exercises 560
19 Learning in Neural and Belief Networks 563
19.1 How the Brain Works 564
·Comparing brains with digital computers 565
19.2 Neural Networks 567
·Notation 567
·Simple computing elements 567
·Network structures 570
·Optimal network structure 572
19.3 Perceptrons 573
·What perceptrons can represent 573
·Learning linearly separable functions 575
19.4 Multilayer Feed-Forward Networks 578
·Back-propagation learning 578
·Back-propagation as gradient descent search 580
·Discussion 583
19.5 Applications of Neural Networks 584
·Pronunciation 585
·Handwritten character recognition 586
·Driving 586
19.6 Bayesian Methods for Learning Belief Networks 588
·Bayesian learning 588
·Belief network learning problems 589
·Learning networks with fixed structure 589
·A comparison of belief networks and neural networks 592
19.7 Summary 593
Bibliographical and Historical Notes 594
Exercises 596
20 Reinforcement Learning 598
20.1 Introduction 598
20.2 Passive Learning in a Known Environment 600
·Naive updating 601
·Adaptive dynamic programming 603
·Temporal difference learning 604
20.3 Passive Learning in an Unknown Environment 605
20.4 Active Learning in an Unknown Environment 607
20.5 Exploration 609
20.6 Learning an Action-Value Function 612
20.7 Generalization in Reinforcement Learning 615
·Applications to game-playing 617
·Application to robot control 617
20.8 Genetic Algorithms and Evolutionary Programming 619
20.9 Summary 621
Bibliographical and Historical Notes 622
Exercises 623
21 Knowledge in Learning 625
21.1 Knowledge in Learning 625
·Some simple examples 626
·Some general schemes 627
21.2 Explanation-Based Learning 629
·Extracting general rules from examples 630
·Improving efficiency 631
21.3 Learning Using Relevance Information 633
·Determining the hypothesis space 633
·Learning and using relevance information 634
21.4 Inductive Logic Programming 636
·An example 637
·Inverse resolution 639
·Top-down learning methods 641
21.5 Summary 644
Bibliographical and Historical Notes 645
Exercises 647
Ⅶ Communicating, perceiving, and acting 649
22 Agents that Communicate 651
22.1 Communication as Action 652
·Fundamentals of language 654
·The component steps of communication 655
·Two models of communication 659
22.2 Types of Communicating Agents 659
·Communicating using Tell and Ask 660
·Communicating using formal language 661
·An agent that communicates 662
22.3 A Formal Grammar for a Subset of English 662
·The Lexicon of ε0 664
·The Grammar ofε0 664
22.4 Syntactic Analysis(Parsing) 664
22.5 Definite Clause Grammar(DCG) 667
22.6 Augmenting a Grammar 668
·Verb Subcategorization 669
·Generative Capacity of Augmented Grammars 671
22.7 Semantic Interpretation 672
·Semantics as DCG Augmentations 673
·The semantics of “john loves Mary” 673
·The semantics ofε1
·Converting quasi-logical form to logical form 677
·Pragmatic Interpretation 678
22.8 ambiguity and Disambiguation 680
·Disambiguation 682
22.9 A Communicating Agent 683
22.10 Summary 684
Bibliographical and Historical Notes 685
Exercises 688
23 Practical Natural Language Processing 691
23.1 Practical Applications 691
·Machine translation 691
·Database access 693
·Information retrieval 694
·Text categorization 695
·Extracting data from text 696
23.2 Efficient Parsing 701
·Extracting parses from the chart: Packing 701
23.3 Scaling Up the Lexicon 703
23.4 Scaling Up the Grammar 705
·Nominal compounds and apposition 706
·Adjective phrases 707
·Determiners 708
·Noun phrases revisited 709
·Clausal complements 710
·Relative clauses 710
·Questions 711
·Handling agrammatical strings 712
23.5 Ambiguity 712
·Syntactic evidence 713
·Lexical evidence 713
·Semantic evidence 713
·Metonymy 714
·Metaphor 715
23.6 Discourse Understanding 715
·The structure of coherent discourse 717
23.7 Summary 719
Bibliographical and Historical Notes 720
Exercises 721
24 Perception 724
24.1 Introduction 724
24.2 Image Formation 725
·Pinhole camera 725
·Lens systems 727
·Photometry of image formation 729
·Spectrophotometry of image formation 730
24.3 Image-Processing Operations for Early Vision 730
·Convolution with linear filters 732
·Edge detection 733
24.4 Extracting3-D Information Using Vision 734
·Motion 735
·Binocular stereopsis 737
·Texture gradients 742
·Shading 743
·Contour 745
24.5 Using Vision for Manipulation and Navigation 749
24.6 Object representation and Recognition 751
·The alignment method 752
·Using projective invariants 754
24.7 Speech Recognition 757
·Signal processing 758
·Defining the overall speech recognition model 760
·The language model: P(words) 760
·The acoustic model: P(signallwords) 762
·Putting the models together 764
·The search algorithm 765
·Training the model 766
24.8 Summary 767
Bibliographical and Historical Notes 767
Exercises 771
25 Robotics 773
25.1 Introduction 773
25.2 Tasks: What Are Robots Good For? 774
·Manufacturing and materials handling 774
·Gofer robots 775
·Hazardous environments 775
·Telepresence and virtual reality 776
·Augmentation of human abilities 776
25.3 Parts: What are Robots Made Of? 777
·Effectors: Tools for action 777
·Sensors: Tools for Perception 782
25.4 Architectures 786
·Classical architecture 787
·Situated automata 788
25.5 Configuration Spaces: A Framework for Analysis 790
·Generalized configuration space 792
·Recognizable Sets 795
25.6 Navigation and Motion Planning 796
·Cell decomposition 796
·Skeletonization methods 798
·Fine-motion planning 802
·Landmark-based navigation 805
·Online algorithms 806
25.7 Summary 809
Bibliographical and Historical Notes 809
Exercises 811
Ⅷ Conclusions 815
26 Philosophical Foundations 817
26.1 The Big Questions 817
26.2 Foundations of Reasoning and Perception 819
26.3 On the Possibility of Achieving Intelligent Behavior 822
·The mathematical objection 824
·The argument from informality 826
26.4 Intentionality and Consciousness 830
·The Chinese Room 831
·The Brain Prosthesis Experiment 835
·Discussion 836
26.5 Summary 837
Bibliographical and Historical Notes 838
Exercises 840
27 AI: Present and Future 842
27.1 Have We Succeeded Yet? 842
27.2 What Exactly Are We Trying to Do? 845
27.3 What If we Do Succeed? 848
A Complexity analysis and O() notation 851
A.1 Asymptotic Analysis 851
A.2 Inherently Hard Problems 852
Bibliographical and Historical Notes 853
B Notes on Languages and Algorithms 854
B.1 Defining Languages with Backus-Naur Form(BNF) 854
B.2 Describing Algorithms with Pseudo-Code 855
·Nondeterminism 855
·Static variables 856
·Functions as values 856
B.3 The Code Repository 857
B.4 Comments 857
Bibliography 859
Index 905
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