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[资源] Cambridge2010Artificial Intelligence - Foundations Of Computational Agents

Contents
Preface xiii
I Agents in theWorld: What Are Agents and How Can They Be
Built? 1
1 Artificial Intelligence and Agents 3
1.1 What Is Artificial Intelligence? . . . . . . . . . . . . . . . . . . 3
1.2 A Brief History of AI . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Agents Situated in Environments . . . . . . . . . . . . . . . . . 10
1.4 Knowledge Representation . . . . . . . . . . . . . . . . . . . . 11
1.5 Dimensions of Complexity . . . . . . . . . . . . . . . . . . . . . 19
1.6 Prototypical Applications . . . . . . . . . . . . . . . . . . . . . 29
1.7 Overview of the Book . . . . . . . . . . . . . . . . . . . . . . . 39
1.8 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
1.9 References and Further Reading . . . . . . . . . . . . . . . . . 40
1.10 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
2 Agent Architectures and Hierarchical Control 43
2.1 Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.2 Agent Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.3 Hierarchical Control . . . . . . . . . . . . . . . . . . . . . . . . 50
2.4 Embedded and Simulated Agents . . . . . . . . . . . . . . . . 59
2.5 Acting with Reasoning . . . . . . . . . . . . . . . . . . . . . . . 60
2.6 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
vii
viii Contents
2.7 References and Further Reading . . . . . . . . . . . . . . . . . 66
2.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
II Representing and Reasoning 69
3 States and Searching 71
3.1 Problem Solving as Search . . . . . . . . . . . . . . . . . . . . . 71
3.2 State Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.3 Graph Searching . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.4 A Generic Searching Algorithm . . . . . . . . . . . . . . . . . . 77
3.5 Uninformed Search Strategies . . . . . . . . . . . . . . . . . . . 79
3.6 Heuristic Search . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
3.7 More Sophisticated Search . . . . . . . . . . . . . . . . . . . . . 92
3.8 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
3.9 References and Further Reading . . . . . . . . . . . . . . . . . 106
3.10 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
4 Features and Constraints 111
4.1 Features and States . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.2 PossibleWorlds, Variables, and Constraints . . . . . . . . . . . 113
4.3 Generate-and-Test Algorithms . . . . . . . . . . . . . . . . . . 118
4.4 Solving CSPs Using Search . . . . . . . . . . . . . . . . . . . . 119
4.5 Consistency Algorithms . . . . . . . . . . . . . . . . . . . . . . 120
4.6 Domain Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . 125
4.7 Variable Elimination . . . . . . . . . . . . . . . . . . . . . . . . 127
4.8 Local Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
4.9 Population-Based Methods . . . . . . . . . . . . . . . . . . . . 141
4.10 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
4.11 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
4.12 References and Further Reading . . . . . . . . . . . . . . . . . 151
4.13 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
5 Propositions and Inference 157
5.1 Propositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
5.2 Propositional Definite Clauses . . . . . . . . . . . . . . . . . . 163
5.3 Knowledge Representation Issues . . . . . . . . . . . . . . . . 174
5.4 Proving by Contradictions . . . . . . . . . . . . . . . . . . . . . 185
5.5 Complete Knowledge Assumption . . . . . . . . . . . . . . . . 193
5.6 Abduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
5.7 Causal Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
5.8 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
5.9 References and Further Reading . . . . . . . . . . . . . . . . . 207
5.10 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
Contents ix
6 Reasoning Under Uncertainty 219
6.1 Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
6.2 Independence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
6.3 Belief Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
6.4 Probabilistic Inference . . . . . . . . . . . . . . . . . . . . . . . 248
6.5 Probability and Time . . . . . . . . . . . . . . . . . . . . . . . . 266
6.6 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274
6.7 References and Further Reading . . . . . . . . . . . . . . . . . 274
6.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275
III Learning and Planning 281
7 Learning: Overview and Supervised Learning 283
7.1 Learning Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . 284
7.2 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . 288
7.3 Basic Models for Supervised Learning . . . . . . . . . . . . . . 298
7.4 Composite Models . . . . . . . . . . . . . . . . . . . . . . . . . 313
7.5 Avoiding Overfitting . . . . . . . . . . . . . . . . . . . . . . . . 320
7.6 Case-Based Reasoning . . . . . . . . . . . . . . . . . . . . . . . 324
7.7 Learning as Refining the Hypothesis Space . . . . . . . . . . . 327
7.8 Bayesian Learning . . . . . . . . . . . . . . . . . . . . . . . . . 334
7.9 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340
7.10 References and Further Reading . . . . . . . . . . . . . . . . . 341
7.11 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342
8 Planning with Certainty 349
8.1 Representing States, Actions, and Goals . . . . . . . . . . . . . 350
8.2 Forward Planning . . . . . . . . . . . . . . . . . . . . . . . . . . 356
8.3 Regression Planning . . . . . . . . . . . . . . . . . . . . . . . . 357
8.4 Planning as a CSP . . . . . . . . . . . . . . . . . . . . . . . . . . 360
8.5 Partial-Order Planning . . . . . . . . . . . . . . . . . . . . . . . 363
8.6 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366
8.7 References and Further Reading . . . . . . . . . . . . . . . . . 367
8.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367
9 Planning Under Uncertainty 371
9.1 Preferences and Utility . . . . . . . . . . . . . . . . . . . . . . . 373
9.2 One-Off Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . 381
9.3 Sequential Decisions . . . . . . . . . . . . . . . . . . . . . . . . 386
9.4 The Value of Information and Control . . . . . . . . . . . . . . 396
9.5 Decision Processes . . . . . . . . . . . . . . . . . . . . . . . . . 399
9.6 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412
9.7 References and Further Reading . . . . . . . . . . . . . . . . . 413
9.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413
x Contents
10 Multiagent Systems 423
10.1 Multiagent Framework . . . . . . . . . . . . . . . . . . . . . . . 423
10.2 Representations of Games . . . . . . . . . . . . . . . . . . . . . 425
10.3 Computing Strategies with Perfect Information . . . . . . . . . 430
10.4 Partially Observable Multiagent Reasoning . . . . . . . . . . . 433
10.5 Group Decision Making . . . . . . . . . . . . . . . . . . . . . . 445
10.6 Mechanism Design . . . . . . . . . . . . . . . . . . . . . . . . . 446
10.7 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449
10.8 References and Further Reading . . . . . . . . . . . . . . . . . 449
10.9 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 450
11 Beyond Supervised Learning 451
11.1 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451
11.2 Learning Belief Networks . . . . . . . . . . . . . . . . . . . . . 458
11.3 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . 463
11.4 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485
11.5 References and Further Reading . . . . . . . . . . . . . . . . . 486
11.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486
IV Reasoning About Individuals and Relations 489
12 Individuals and Relations 491
12.1 Exploiting Structure Beyond Features . . . . . . . . . . . . . . 492
12.2 Symbols and Semantics . . . . . . . . . . . . . . . . . . . . . . 493
12.3 Datalog: A Relational Rule Language . . . . . . . . . . . . . . 494
12.4 Proofs and Substitutions . . . . . . . . . . . . . . . . . . . . . . 506
12.5 Function Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . 512
12.6 Applications in Natural Language Processing . . . . . . . . . . 520
12.7 Equality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 532
12.8 Complete Knowledge Assumption . . . . . . . . . . . . . . . . 537
12.9 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541
12.10 References and Further Reading . . . . . . . . . . . . . . . . . 542
12.11 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 542
13 Ontologies and Knowledge-Based Systems 549
13.1 Knowledge Sharing . . . . . . . . . . . . . . . . . . . . . . . . . 549
13.2 Flexible Representations . . . . . . . . . . . . . . . . . . . . . . 550
13.3 Ontologies and Knowledge Sharing . . . . . . . . . . . . . . . 563
13.4 Querying Users and Other Knowledge Sources . . . . . . . . . 576
13.5 Implementing Knowledge-Based Systems . . . . . . . . . . . . 579
13.6 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591
13.7 References and Further Reading . . . . . . . . . . . . . . . . . 591
13.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592
Contents xi
14 Relational Planning, Learning, and Probabilistic Reasoning 597
14.1 Planning with Individuals and Relations . . . . . . . . . . . . 598
14.2 Learning with Individuals and Relations . . . . . . . . . . . . 606
14.3 Probabilistic Relational Models . . . . . . . . . . . . . . . . . . 611
14.4 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 618
14.5 References and Further Reading . . . . . . . . . . . . . . . . . 618
14.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 620
V The Big Picture 623
15 Retrospect and Prospect 625
15.1 Dimensions of Complexity Revisited . . . . . . . . . . . . . . . 625
15.2 Social and Ethical Consequences . . . . . . . . . . . . . . . . . 629
15.3 References and Further Reading . . . . . . . . . . . . . . . . . 632
A Mathematical Preliminaries and Notation 633
A.1 Discrete Mathematics . . . . . . . . . . . . . . . . . . . . . . . . 633
A.2 Functions, Factors, and Arrays . . . . . . . . . . . . . . . . . . 634
A.3 Relations and the Relational Algebra . . . . . . . . . . . . . . . 635
Bibliography 637
Index 653
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