Artificial Intelligence in the 21st Century 2ed

darrenxu

[color=rgba(0, 0, 0, 0.87)]“21世纪人工智能”英文版Stephen Lucci ISBN：1942270003 | 2015 | 850页| True PDF | 34 MB本文提供了一个全面，丰富多彩的，最新的和可访问的AI的演示，而不牺牲理论基础。它包括许多示例，应用程序，全彩图像和人感兴趣的盒子，以增强学生的兴趣。高级主题涵盖神经网络，遗传算法和复杂的棋盘游戏。资源，模拟和图书中附带的DVD随附DVD。教师资源可在采用后获得。第二版的特点：•关于机器人，机器学习和电脑游戏的新的独立章节•引入重要的AI概念，例如专家系统，用于视频游戏，神经网络，机器学习，更多地通过实际应用程序包括DVD与资源，模拟和图书数字许多教师资源，例如解决方案，Microsoft PP幻灯片等可用于采纳者

Contents
Preface xvii
Acknowledgments xxiii
Credits xxvii
1
Chapter 1 Overview of Artificial Intelligence 3
1.0 Introduction
1.0.1 What is Artificial Intelligence? 4
1.0.2 What is Thinking? What is intelligence? 5
1.1 The Turing Test 7
1.1.1 Definition of the Turing Test 7
1.1.2 Controversies and Criticisms of the Turing Test 9
1.2 Strong AI Versus Weak AI 10
1.3 Heuristics 12
1.3.1 The Diagonal of a Rectangular Solid: Solving a Simpler, But Related Problem 12
1.3.2 The Water Jug Problem: Working Backward 13
1.4 Identifying Problems Suitable for AI 14
1.5 Applications and Methods 16
1.5.1 Search Algorithms and Puzzles 17
1.5.2 Two-Person Games 19
1.5.3 Automated Reasoning 19
1.5.4 Production Rules and Expert Systems 20
1.5.5 Cellular Automata 21
1.5.6 Neural Computation 22
1.5.7 Genetic Algorithms 23
1.5.8 Knowledge Representation 23
1.5.9 Uncertainty Reasoning 25
viii ■ Contents
1.6 Early History of AI 25
1.6.1 Logicians and Logic Machines 27
1.7 Recent History of AI to the Present 29
1.7.1 Games 29
1.7.2 Expert Systems 30
1.7.3 Neural Computing 31
1.7.4 Evolutionary Computation 31
1.7.5 Natural Language Processing 32
1.7.6 Bioinformatics 34
1.8 AI In the New Millennium 34
1.9 Chapter Summary 36
PART II: FUNDAMENTALS 43
Chapter 2 Uninformed Search 45
2.0 Introduction: Search in Intelligent Systems 46
2.1 State-Space Graphs 47
2.1.1 The False Coin Problem 47
2.2 Generate-and-Test Paradigm 48
2.2.1 Backtracking 50
2.2.2 The Greedy Algorithm 53
2.2.3 The Traveling Salesperson Problem 55
2.3 Blind Search Algorithms 56
2.3.1 Depth First Search 57
2.3.2 Breadth First Search 58
2.4 Implementing and Comparing Blind Search Algorithms 60
2.4.1 Implementing A Depth First Search Solution 61
2.4.2 Implementing A Breadth First Search Solution 62
2.4.3 Measuring Problem-Solving Performance 62
2.4.4 Comparing Dfs and Bfs 64
2.5 Chapter Summary 67
Chapter 3 Informed Search 75
3.0 Introduction 75
3.1 Heuristics 77
3.2 Informed Search Algorithms (Part I) – Finding Any Solution 83
3.2.1 Hill Climbing 83
3.2.2 Steepest-Ascent Hill Climbing 84
3.3 The Best-First Search 86
3.4 The Beam Search 89
3.5 Additional Metrics for Search Algorithms 90
3.6 Informed Search (Part 2) – Finding An Optimal Solution 91
3.6.1. Branch and Bound 92
3.6.2 Branch and Bound With Underestimates 95
Contents ■ ix
3.6.3 Branch and Bound With Dynamic Programming 98
3.6.4 The A* Search 99
3.7 Informed Search (Part 3) – Advanced Search Algorithms 100
3.7.1 Constraint Satisfaction Search 100
3.7.2 And/Or Trees 101
3.7.3 The Bidirectional Search 102
3.8 Chapter Summary 105
Chapter 4 Search Using Games 111
4.0 Introduction 112
4.1 Game Trees and Minimax Evaluation 112
4.1.1 Heuristic Evaluation 114
4.1.2 Minimax Evaluation of Game Trees 115
4.2 Minimax With Alpha-Beta Pruning 118
4.3 Variations and Improvements To Minimax 123
4.3.1 Negamax Algorithm 123
4.3.2 Progressive Deepening 125
4.3.3 Heuristic Continuation and the Horizon Effect 125
4.4 Games of Chance and the Expectiminimax Algorithm 125
4.5 Game Theory 127
4.5.1 The Iterated Prisoner’s Dilemma 129
4.6 Chapter Summary 129
Chapter 5 Logic in Artificial Intelligence 137
5.0 Introduction 138
5.1 Logic and Representation 139
5.2 Propositional Logic 140
5.2.1 Propositional Logic – Basics 140
5.2.2 Arguments in the Propositional Logic 144
5.2.3 Proving Arguments in the Propositional Logic Valid – A Second Approach 145
5.3 Predicate Logic – Introduction 147
5.3.1 Unification in the Predicate Logic 148
5.3.2 Resolution in the Predicate Logic 150
5.2.3 Converting A Predicate Expression to Clause Form 152
5.4 Several Other Logics 155
5.4.1 Second Order Logic 156
5.4.2 Non-Monotonic Logic 156
5.4.3 Fuzzy Logic 157
5.4.4 Modal Logic 157
5.5 Chapter Summary 158
Chapter 6 Knowledge Representation 167
6.0 Introduction 168
6.1 Graphical Sketches and the Human Window 171
x
■ Contents
6.2 Graphs and the Bridges of Königsberg Problem 174
6.3 Search Trees 174
6.3.1 Decision Tree 175
6.4 Representational Choices 176
6.5 Production Systems 179
6.6 Object Orientation 180
6.7 Frames 181
6.8 Scripts and the Conceptual Dependency System 183
6.9 Semantic Networks 186
6.10 Associations 188
6.11 More Recent Approaches 189
6.11.1 Concept Maps 189
6.11.2 Conceptual Graphs 190
6.11.3 Baecker’s Work 191
6.12 Agents: Intelligent or Otherwise 192
6.12.1 A Little Agent History 195
6.12.2 Contemporary Agents 197
6.12.3 The Semantic Web 198
6.12.4 The Future – According To IBM 199
6.12.5 Author’s Perspective 199
6.13 Chapter Summary 200
Chapter 7 Production Systems 207
7.0 Introduction 208
7.1 Background 208
7. 1.1 Strong Methods Vs. Weak Methods 209
7.2 Basic Examples 210
7.3 The CarBuyer System 213
7.3.1 Advantages of Production Systems 216
7.4 Production Systems and Inference Methods 218
7.4.1 Conflict Resolution 221
7.4.2 Forward Chaining 223
7.4.3 Backward Chaining 225
7.5 Production Systems and Cellular Automata 230
7.6 Stochastic Processes and Markov Chains 232
7.7 Chapter Summary 233
PART III: KNOWLEDGE-BASED SYSTEMS 237
Chapter 8 Uncertainty in AI 239
8.0 Introduction 240
8.1 Fuzzy Sets 240
8.2 Fuzzy Logic 243
8.3 Fuzzy Inferences 243
Contents ■ xi
8.4 Probability Theory and Uncertainty 247
8.5 Chapter Summary 252
Chapter 9 Expert Systems 257
9.0 Introduction 258
9. 1 Background 258
9.1.1 Human and Machine Experts 259
9.2 Characteristics of Expert Systems 265
9.3 Knowledge Engineering 267
9.4 Knowledge Acquisition 269
9.5 Classic Expert Systems 271
9.5.1 Dendral 271
9.5.2 Mycin 272
9.5.3 Emycin 274
9.5.4 Prospector 275
9.5.5 Fuzzy Knowledge and Bayes’ Rule 278
9.6 Methods for Efficiency 279
9.6.1 Demon Rules 279
9.6.2 The Rete Algorithm 280
9.7 Case-Based Reasoning 281
9.8 More Recent Expert Systems 287
9.8.1 Systems for Improving Employment Matching 287
9.8.2 An Expert System for Vibration Fault Diagnosis 287
9.8.3 Automatic Dental Identification 288
9.8.4 More Expert Systems Employing Case-Based Reasoning 289
9.9 Chapter Summary 289
Chapter 10 Machine Learning :
Part I Inductive Learning with Decision Trees 297
10.0 Introduction 298
10.1 Machine Learning: A Brief Overview 298
10.2 The Role of Feedback in Machine Learning Systems 300
10.3 Inductive Learning 301
10.4 Learning With Decision Trees 303
10.5 Problems Suitable for Decision Trees 304
10.6 Entropy 305
10.7 Constructing A Decision Tree With ID3 306
10.8 Issues Remaining 309
10.9 Chapter Summary 309
Chapter 11 Machine Learning : Part II Neural Networks 313
11.0 Introduction 314
11.1 Rudiments of Artificial Neural Networks 314
11.2 McCulloch-Pitts Network 316
xii ■ Contents
11.3 The Perceptron Learning Rule 317
11.4 The Delta Rule 327
11.5 Backpropagation 333
11.6 Implementation Concerns 340
11.6.1 Pattern Analysis 344
11.6.2 Training Methodology 345
11.7 Discrete Hopfield Networks 347
11.8 Application Areas 351
11.9 Chapter Summary 360
Chapter 12 Search Inspired by Mother Nature 369
12.0 Introduction 370
12.1 Simulated Annealing 371
12.2 Genetic Algorithms 374
12.3 Genetic Programming 381
12.4 Tabu Search 385
12.5 Ant Colony Optimization 388
12.6 Chapter Summary 392
PART IV: ADVANCED TOPICS 401
Chapter 13 Natural Language Understanding 403
13.0 Introduction 404
13.1 Overview: the Problems and Possibilities of Language 404
13.1.1 Ambiguity 405
13.2 History of Natural Language Processing (NLP) 406
13.2.1 Foundations (1940s and 1950s) 407
13.2.2 Symbolic vs. Stochastic Approaches (1957–1970) 407
13.2.3 The Four Paradigms: 1970–1983 408
13.2.4 Empiricism and Finite-State Models 409
13.2.5 The Field Comes Together: 1994–1999 409
13.2.6 The Rise of Machine Learning 410
13.3 Syntax and Formal Grammars 410
13.3.1 Types of Grammars 411
13.3.2 Syntactic Parsing: the CYK Algorithm 415
13.4 Semantic Analysis and Extended Grammars 417
13.4.1 Transformational Grammar 417
13.4.2 Systemic Grammar 418
13.4.3 Case Grammars 419
13.4.4 Semantic Grammars 420
13.4.5 Schank’s Systems 421
13.5 Statistical Methods in NLP 425
13.5.1 Statistical Parsing 425
13.5.2 Machine Translation (Revisited) and IBM’s Candide System 426
13.5.3 Word Sense Disambiguation 427
Contents ■ xiii
13.6 Probabilistic Models for Statistical NLP 428
13.6.1 Hidden Markov Models 428
13.6.2 The Viterbi Algorithm 430
13.7 Linguistic Data Collections for Statistical NLP 431
13.7.1 The Penn Treebank Project 431
13.7.2 WordNet 432
13.7.3 Models of Metaphor in NLP 433
13.8 Applications: Information Extraction and Question Answering Systems 435
13.8.1 Question Answering Systems 435
13.8.2 Information Extraction 440
13.9 Present and Future Research (According To Charniak) 440
13.10 Speech Understanding 441
13.10.1 Speech Understanding Techniques 441
13.11 Applications of Speech Understanding 445
13.11.1 Dragon’s NaturallySpeaking System and
Windows’ Speech Recognition System 446
13.11.2 CISCO’s Voice System 449
13.12 Chapter Summary 450
Chapter 14 Automated Planning 457
14.0 Introduction 458
14.1 The Problem of Planning 458
14.1.1 Planning Terminology 459
14.1.2 Examples of Planning Applications 459
14.2 A Brief History and A Famous Problem 465
14.2.1 The Frame Problem 466
14.3 Planning Methods 467
14.3.1 Planning As Search 467
14.3.2 Partially Ordered Planning 471
14.3.3 Hierarchical Planning 473
14.3.4 Case-Based Planning 474
14.3.5 A Potpourri of Planning Methods 475
14.4 Early Planning Systems 476
14.4.1 Strips 476
14.4.2 Noah 477
14.4.3 Nonlin 478
14.5 More Modern Planning Systems 479
14.5.1 O-Plan 479
14.5.2 Graphplan 481
14.5.3 A Potpourri of Planning Systems 482
14.5.4 A Planning Approach To Learning Systems 483
14.5.5 The Sci Box Automated Planner 484
14.6 Chapter Summary 487
xiv ■ Contents
PART V: THE PRESENT AND FUTURE 493
Chapter 15 Robotics 495
15.0 Introduction 496
15.1 History: Serving, Emulating, Enhancing, and Replacing Man 499
15.1.1 Robot Lore 499
15.1.2 Early Mechanical Robots 500
15.1.3 Robots in Film and Literature 503
15.1.4 Twentieth-Century Robots 503
15.2 Technical Issues 509
15.2.1 Robot Components 510
15.2.2 Locomotion 513
15.2.3 Path Planning for A Point Robot 515
15.2.4 Mobile Robot Kinematics 515
15.3 Applications: Robotics in the Twenty-First Century 518
15.4 Chapter Summary 527
Chapter 16 Advanced Computer Games 531
16.0 Introduction 532
16.1 Checkers: From Samuel to Schaeffer 532
16.1.1 Heuristic Methods for Learning in the Game of Checkers 536
16.1.2 Rote Learning and Generalization 539
16.1.3 Signature Table Evaluations and Book Learning 540
16.1.4 World Championship Checkers With Schaeffer’s Chinook 540
16.1.5 Checkers is Solved 542
16.2 Chess: The Drosophila of AI 545
16.2.1 Historical Background of Computer Chess 546
16.2.2 Programming Methods 547
16.2.3 Beyond the Horizon 555
16.2.4 Deep Thought and Deep Blue Against Grandmaster
Competition: 1988–1995 555
16.3 Contributions of Computer Chess to Artificial Intelligence 557
16.3.1 Search in Machines 557
16.3.2 Search in Man vs. Machine 558
16.3.3 Heuristics, Knowledge, and Problem-Solving 559
16.3.4 Brute Force: Knowledge vs. Search; Performance vs. Competence 559
16.3.5 Endgame Databases and Parallelism 560
16.3.6 Author Contributions 561
16.4 Other Games 565
16.4.1 Othello 565
16.4.2 Backgammon 567
16.4.3 Bridge 567
16.4.4 Poker 569
Contents ■ xv
16.5 Go: The New Drosophila of AI? 570
16.5.1 The Stars of Advanced Computer Games 571
16.6 Chapter Summary 573
Chapter 17 Reprise 583
17.0 Introduction 584
17.1 Recapitulation — Part I 584
17.2 Prometheus Redux 585
17.3 Recapitulation—Part II: Present AI Accomplishments 587
17.4 IBM Watson-Jeopardy Challenge 591
17.5 AI in the 21st Century 595
17.6 Chapter Summary 597
Appendix A. Example with CLIPS: The Expert System Shell A1–A5
Appendix B. Implementation of the Viterbi
Algorithm for Hidden Markov Chains. B1–B4
Appendix C. The Amazing Walter Shawn Browne C1–C3
Appendix D. On Companion Data Disc D1
Appendix E. Solutions to Selected Odd Exercises E1
Index 601


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