Artificial Intelligence in the 21st Century

739 Pages·2022·61.185 MB·English

by  Stephen Lucci, Sarhan M. Musa, Danny Kopec

Artificial Intelligence
in the 21st Century
Third Edition
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Artificial Intelligence
in the 21ST Century
Third Edition
Stephen Lucci, Ph.D.
The City College of New York, CUNY
Sarhan M. Musa, Ph.D.
Prairie View A&M University
Danny Kopec, Ph.D.
Brooklyn College, CUNY
Mercury Learning and Information
Dulles, Virginia
Boston, Massachusetts
New Delhi
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S. Lucci, S. Musa, and D. Kopec. Artificial Intelligence in the 21ST Century, Third Edition.
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To the memory of my parents,
Louis and Connie Lucci,
Both of whom always encouraged my education.
Stephen Lucci
To the memory of my parents,
Mahmoud Musa and Fatmeh Hussein,
Both of whom always on my mind and forever in my heart.
Sarhan Musa
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Contents
Preface xix
Acknowledgments ( Three Editions) xxi
Credits for the 3rd Edition xxv
Part 1: Introduction 1
Chapter 1 Overview of Artificial Intelligence 3
1.0 Introduction 4
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 12
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
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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 63
2.4.3 Measuring Problem-Solving Performance 63
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
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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
5.3.3 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
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