Artificial Intelligence. A Systems Approach

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Artificial Intelligence. A Systems Approach
TABLE OF CONTENTS
Chapter 1 The History of AI 1-19
What is Intelligence? 1
The Search for Mechanical Intelligence 2
The Very Early Days (the early 1950’s) 3
Alan Turing 3
AI, Problem Solving and Games 4
Artificial Intelligence Emerges as a Field 5
The Dartmouth AI Summer Research Project 5
Building Tools for AI 6
The Focus on Strong AI 6
Constrained Applications 7
Bottom-Up Approaches Emerge 7
AI’s Winter 8
Results-Oriented Applications 8
Additional AI Tools Emerge 9
Neat vs. Scruffy Approaches 9
AI Remerges 10
The Silent Return 10
Messy and Scruffy Approaches Take Hold 10
Agent Systems 12
AI Inter-disciplinary R&D 12
Systems Approach 13
Overview of this Book 15
Uninformed Search 15
Informed Search 15
AI and Games 15
Knowledge Representation 16
Machine Learning 16
Evolutionary Computation 16
Neural Networks Part 1 16
Neural Networks Part 2 17
Intelligent Agents 17
Biologically Inspired and Hybrid Models 17
Languages of AI 17
Chapter Summary 18
References 18
Resources 18
Exercises 19
Chapter 2 Uninformed Search 21-48
Search and AI 21
Classes of Search 22
General State Space Search 22
Search in a Physical Space 22
Search in a Puzzle Space 23
Search in an Adversarial Game Space 25
Trees, Graphs and Representation 27
Uninformed Search 29
Helper APIs 30
General Search Paradigms 31
Depth-First Search 31
Depth-Limited Search 34
Iterative Deepening Search 36
Breadth-First Search 39
Bidirectional Search 42
Uniform-Cost Search 42
Improvements 45
Algorithm Advantages 46
Chapter Summary 46
Algorithms Summary 46
References 47
Exercises 47
Chapter 3 Informed Search 49-88
Search and AI 49
Best-First Search 50
Best-First Search and the N-Queens Problem 50
Best-First Search Implementation 52
Variants of Best-First Search 56
A* Search 57
A* Search and the Eight Puzzle 59
Eight Puzzle Representation 59
A* Search Implementation 61
Eight Puzzle Demonstration with A* 64
A* Variants 65
Applications of A* Search 65
Hill Climbing Search 65
Simulated Annealing 66
The Traveling Salesman Problem (TSP) 68
TSP Tour Representation 68
Simulated Annealing Implementation 70
Simulated Annealing Demonstration 73
Tabu Search 75
Tabu Search Implementation 77
Tabu Search Demonstration 79
Tabu Search Variants 80
Constraint Satisfaction 81
Graph Coloring as a CSP 81
Scheduling as CSP 83
Constraint Satisfaction Problems 84
Generate and Test 84
Backtracking 84
Forward Checking and Look Ahead 84
Min-Conflicts Search 86
Chapter Summary 86
Algorithms Summary 86
References 86
Resources 87
Exercises 87
Chapter 4 AI and Games 89-142
Two Player Games 89
The Minimax Algorithm 92
Minimax and Tic-Tac-Toe 95
Minimax Implementation for Tic-Tac-Toe 98
Minimax with Alpha-Beta Pruning 101
Classical Game AI 106
Checkers 106
Checker Board Representation 107
Techniques used in Checkers Programs 107
Opening Books 108
Static Evaluation Function 108
Search Algorithm 108
Move History 108
Endgame Database 109
Chess 109
Chess Board Representation 110
Techniques used in Chess Programs 110
Opening Book Database 110
Minimax Search with Alpha Beta Pruning 111
Static Board Evaluation 111
Othello 112
Techniques used in Othello Programs 112
Opening Knowledge 112
Static Evaluation Function 112
Search Algorithm 113
Endgames 113
Other Algorithms 113
Go 114
Go Board Representation 114
Techniques used in Go Programs 114
Opening Moves 115
Move Generation 115
Evaluation 115
Endgame 116
Backgammon 116
Techniques used in Backgammon Programs 116
Neurogammon 116
TD-Gammon 117
Poker 118
Loki – A learning Poker Player 119
Scrabble 120
Video Game AI 121
Applications of AI Algorithms in Video Games 122
Movement and Pathfinding 123
Table Lookup with Offensive and Defensive Strategy 123
NPC Behavior 129
Static State Machines 130
Layered Behavior Architectures 131
Other Action-Selection Mechanisms 132
Team AI 132
Goals and Plans 134
Real-Time Strategy AI 136
Rules-Based Programming 136
Chapter Summary 139
References 139
Resources 140
Exercises 141
Chapter 5 Knowledge Representation 143-170
Introduction 143
Types of Knowledge 144
The Role of Knowledge 144
Semantic Nets 145
Frames 146
Proposi tional Logic 149
Deductive Reasoning with Propositional Logic 151
Limitations of Propositional Logic 152
First Order Logic (Predicate Logic) 152
Atomic Sentences 153
Compound Sentences 154
Variables 154
Quantifiers 155
First-Order Logic and Prolog 155
Simple Example 155
Information Retrieval and KR 157
Representing and Reasoning about an Environment 159
Semantic Web 163
Computational Knowledge Discovery 165
The BACON System 165
Automatic Mathematician 166
Ontology 167
Communication of Knowledge 167
Common Sense 168
Summary 169
References 169
Resources 169
Exercises 170
Chapter 6 Machine Learning 171-193
Machine Learning Algorithms 171
Supervised Learning 172
Learning with Decision Trees 172
Creating a Decision Tree 174
Characteristics of Decision Tree Learning 176
Unsupervised Learning 176
Markov Models 177
Word Learning with Markov Chains 177
Word Generation with Markov Chains 179
Markov Chain Implementation 180
Other Applications of Markov Chains 184
Nearest Neighbor Classification 185
1NN Example 186
k-NN Example 188
Summary 192
Resources 192
Exercises 192
Chapter 7 Evolutionary Computation 195-247
Short History of Evolutionary Computation 195
Evolutionary Strategies 196
Evolutionary Programming 197
Genetic Algorithms 197
Genetic Programming 198
Biological Motivation 199
Genetic Algorithms 200
Genetic Algorithm Overview 200
Genetic Algorithm Implementation 204
Genetic Programming 212
Genetic Programming Algorithm 212
Genetic Programming Implementation 215
Evolutionary Strategies 220
Evolutionary Strategies Algorithm 221
Evolutionary Strategies Implementation 223
Differential Evolution 227
Differential Evolution Algorithm 228
Differential Evolution Implementation 230
Particle Swarm Optimization 236
Particle Swarm Algorithm 236
Particle Swarm Implementation 238
Evolvable Hardware 244
Summary 244
References 245
Resources 245
Exercises 245
Chapter 8 Neural Networks I 249-287
Short History of Neural Networks 249
Biological Motiviation 250
Fundamentals of Neural Networks 251
Single Layer Perceptrons 252
Multi-Layer Perceptrons 254
Supervised vs. Unsupervised Learning Algorithms 257
Binary vs. Continuous Inputs and Outputs 257
The Perceptron 257
Perceptron Learning Algorithm 259
Perceptron Implementation 260
Least-Mean-Square (LMS) Learning 262
LMS Learning Algorithm 262
LMS Implementation 263
Learning with Backpropagation 265
Backpropagation Algorithm 267
Backpropagation Implementation 268
Tuning Backpropagation 274
Training Variants 274
Weight Adjustment Variants 274
Probabilistic Neural Networks 275
PNN Algorithm 276
PNN Implementation 277
Other Neural Network Architectures 281
Time Series Processing Architecture 281
Recurrent Neural Network 283
Tips for Building Neural Networks 283
Defining the Inputs 283
Defining the Outputs 284
Choice of Activation Functions 284
Number of Hidden Layers 285
Chapter Summary 285
References 285
Exercises 285
Chapter 9 Neural Networks II 289-328
Unsupervised Learning 289
Hebbian Learning 290
Hebb’s Rule 291
Hebb Rule Implementation 292
Simple Competitive Learning 296
Vector Quantization 297
Vector Quantization Implementation 298
k-Means Clustering 304
k-Means Algorithm 305
k-Means Implementation 307
Adaptive Resonance Theory 313
ART-1 Algorithm 314
ART-1 Implementation 316
Hopfield Auto-Associative Model 322
Hopfield Auto-Associator Algorithm 323
Hopfield Implementation 324
Summary 327
References 328
Exercises 328
Chapter 10 Robotics and AI 329-348
Introduction to Robotics 329
What is a Robot? 330
A Sampling from the Spectrum of Robotics 331
Taxonomy of Robotics 332
Fixed 333
Legged 333
Wheeled 333
Underwater 333
Aerial 333
Other Types of Robots 334
Hard vs. Soft Robotics 334
Braitenburg Vehicles 334
Natural Sensing and Control 336
Perception with Sensors 337
Actuation with Effectors 338
Robotic Control Systems 338
Simple Control Architectures 339
Reactive Control 340
Subsumption 340
Other Control Systems 342
Movement Planning 342
Complexities of Motion Planning 342
Cell Decomposition 343
Potential Fields 344
Group or Distributed Robotics 345
Robot Programming Languages 346
Robot Simulators 346
Summary 346
References 346
Resources 347
Exercises 347
Chapter 11 Intelligent Agents 349-391
Anatomy of an Agent 350
Agent Properties and AI 351
Rationale 352
Autonomous 352
Persistent 352
Communicative 352
Cooperative 353
Mobile 353
Adaptive 353
Agent Environments 353
Agent Taxonomies 356
Interface Agents 356
Virtual Character Agents 357
Entertainment Agents 358
Game Agents 358
ChatterBots 360
Eliza and Parry 360
AIML 361
Mobile Agents 362
User Assistance Agent 364
Email Filtering 364
Information Gathering and Filtering 365
Other User-Assistance Applications 365
Hybrid Agent 366
Agent Architectures 366
What is Architecture? 366
Types of Architectures 367
Reactive Architectures 367
Deliberative Architectures 368
Blackboard Architectures 369
BDI Architecture 370
Hybrid Architectures 371
Mobile Architectures 371
Architecture Description 372
Subsumption Architecture (Reactive) 372
Behavior Networks (Reactive) 373
ATLANTIS (Deliberative) 375
Homer (Deliberative) 376
BB1 (Blackboard) 377
Open Agent Architecture (Blackboard) 377
Procedural Reasoning System (BDI) 378
Aglets (Mobile) 379
Messengers (Mobile) 380
SOAR (Hybrid) 382
Agent Languages 382
Telescript 382
Aglets 383
Obliq 384
Agent TCL 384
Traditional Languages 385
Agent Communication 385
Knowledge Query and Manipulation Language (KQML) 385
FIPA Agent Communication Language 388
Extensible Markup Language (XML) 388
Summary 389
Resources 389
References 390
Exercises 391
Chapter 12 Biologically Inspired and Hybrid Models 393-432
Cellular Automata 393
One Dimensional CA 394
Two Dimensional CA 395
Conway Application 396
Turing Completeness 398
Emergence and Organization 398
Artificial Immune Systems 398
Self-Management Capabilities 399
Touchpoints 400
Touchpoint Autonomic Managers 400
Orchestrating Autonomic Managers 401
Integrated Management Console 401
Autonomic Summary 402
Artificial Life 402
Echo 403
Tierra 403
Simulated Evolution 403
Environment 403
The Bug (or Agent) 404
Variations of Artificial Life 408
Lindenmayer Systems 408
Fuzzy Logic 410
Introduction to Fuzzy Logic 410
Fuzzy Logic Mapping 411
Fuzzy Logic Operators 414
Fuzzy Control 415
Evolutionary Neural Networks 416
Genetically Evolved Neural Networks 416
Simulation Evolution Example 419
Ant Colony Optimization 423
Traveling Salesman Problem 423
Path Selection 425
Pheromone Intensification 425
Pheromone Evaporation 426
New Tour 426
Sample Usage 426
ACO Parameters 430
Affective Computing 430
Characterizing Human Emotion 430
Synthesizing Emotion 431
Resources 432
Chapter 13 The Languages of AI 433-483
Language Taxonomy 433
Functional Programming 434
Imperative Programming 437
Object Oriented Programming 438
Logic Programming 441
Languages of AI 442
The LISP Language 443
The History of the LISP Language 443
Overview of the LISP Language 444
Data Representation 444
Simple Expressions 444
Predicates 445
Variables 445
List Processing 445
Programs as Data 447
Conditions 447
Functions in LISP 448
LISP Summary 451
The Scheme Language 451
History of Scheme 452
Overview of the Scheme Language 452
Data Representation 452
Simple Expressions 452
Predicates 453
Variables 453
List Processing 454
Conditions 455
Iteration and Maps 456
Procedures in Scheme 457
Scheme Summary 460
The POP-11 Language 460
History of POP-11 460
Overview of the POP-11 Language 460
Data Representation 460
Predicates 461
Simple Expressions 461
Variables 462
List Processing 462
Conditions 463
Iteration and Maps 464
Pattern Matching 465
Procedures in POP-11 465
POP-11 Summary 468
Prolog 468
History of Prolog 469
Overview of the Prolog Language 469
Data Representation 469
List Processing 470
Facts, Rules, and Evaluation 471
Arithmetic Expressions 478
Prolog Summary 480
Other Languages 480
Chapter Summary 481
References 481
Resources 482
Exercises 482
About the CD-ROM 485
Index 487-498

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