Artificial Intelligence: A Systems Approach (w/CDROM)(Computer Science) (Engineering)(AI)

518 Pages·2007·10.66 MB·English

by  M. Tim Jones

ARTIFICIAL
INTELLIGENCE
A Systems Approach
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ARTIFICIAL
INTELLIGENCE

A Systems Approach
M. TIM JONES
INFINITY SCIENCE PRESS LLC
Hingham, Massachusetts
New Delhi
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M. Tim Jones. Artificial Intelligence: A Systems Approach
ISBN: 978-0-9778582-3-1
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Library of Congress Cataloging-in-Publication Data
JONES, M. TIM.
Artificial intelligence : a systems approach / M. Tim Jones.
p. cm.
Includes index.
ISBN-13: 978-0-9778582-3-1 (hardcover with cd-rom : alk. paper)
1. Artificial intelligence--Data processing. 2. Artificial intelligence--Mathematical models. I. Title.
Q336.J68 2008
006.3--dc22
2007045869
7 8 9 0 4 3 2 1
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D
EDICATION
This book is dedicated to my wonderful wife, Jill, without whom this book would not be
possible. I’m also indebted to my parents Maury and Celeta, who instilled in me a desire to
learn and wonder.
A
CKNOWLEDGMENTS
At the time of this writing, AI is celebrating its 50th anniversary. It was August of 1956 when
researchers met at the Dartmouth Summer Research Project on Artificial Intelligence with
the agenda of creating intelligent machines. In the 50 years that followed, AI has become a
genuine field of study, but the road has not been without its bumps.
Acknowledging all those who’ve contributed to AI would fill a book much larger than
this. But I’d like to personally recognize John McCarthy for introducing AI in 1955 (at the
Dartmouth Summer Project) and for having created the wonderful Lisp programming
language.
T C
ABLE OF ONTENTS
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