Contents
Preface 4
Chapter 1 13
Introduction to AI 13
1.1 What is A. I.? 14
1.2 A. I. Definition. 15
1.3 Historical Developments in AI. 16
1.4 AI Applications 18
1.5 Criticisms of AI 20
1.6 Strong and Weak A.I 22
1.7 A. I. Representation 23
1.8 Properties of Knowledge Representation 24
1.9 Exercises 26
Chapter 2 27
A.I. Search Techniques 27
2.1 A.I. Techniques 28
2.2 Tile Puzzle –State Space Representation 30
2.3 Tile Puzzle – Depth First Search 33
2.4 Tile Puzzle –Breadth First Search 36
2.5 Weak Methods 37
2.6 Tile Puzzle –Best First Search 38
2.7 A Star Algorithm (A*) 40
2.9 Summary 45
2. 10 Exercises 51
Chapter 3 52
Game Playing 52
3.1 Why Games? 53
3.2 Tic – Tac -
3.3 Tic – Tac – Toe – Search tree. 56
3.4 Tic – Tac – Toe – MiniMax Method. 61
3.5 MiniMax Method – Alpha Beta Pruning 66
3.6 Waiting for Quiescence 70
3.7 Summary 72
3. 8 Exercises:-
Chapter 4 75
Predicate Calculus 75
4.1 Issues in Knowledge Representation 76
4.2 Propositional Calculus 77
4.3 Propositional Calculus Example 78
4.4 Limitation of Propositional Calculus 79
4.5 ISA Hierarchy 80
4.5 Application of Predicate Calculus 81
4.6 Resolution 87
4.7 Summary 88
4. 8 Exercises:-
Chapter 5 91
Non Monotonic Logic 91
5.1 Introduction to Non Monotonic logic 92
5.2 Truth Maintenance Systems 93
Justification Based TMS (JTMS) 94
Assumption Based TMS (ATMS) 96
Logic Based TMS (LTMS) 98
5.3 Fuzzy Logic 99
5.4 Semantic Nets and Frames 100
5.5 Frames 103
5.5 Conceptual Dependency 103
Primitive Acts of CD theory 104
Rules for CD representation 105
5.6 Summary 112
5.10 Exercises 115
Chapter 6 116
Learning and Planning 116
6.1 Learning and Artificial Intelligence 117
6.2 Rote Learning 117
6.3 Learning by Problem Solving 118
6.4 Discovery 120
6.5 Planning 120
6.6 Blocks World Problem 121
STRIPS stands for Stanford Research Institute Problem Solver 125
.STRIPS ALGORITHM:-
6.7 Forward and Backward Planning 126
Which Planning to Use? 127
6.8 Non Linear Planning 128
6.9 Hierarchical Planning 133
Approach. 134
6.10 Summary 135
Types of Learning: 135
Difference between discovery and learning 136
Types of discovery systems 136
Planning 136
Components of planning system (steps in planning system) 136
GOAL STACK PLANNING 137
STRIPS: Stands for Stanford Research Institute Problem Solver 137
BLOCKS WORLD Problem 137
Types of Planning: 138
Linear Planning: 138
Non-
Hierarchical planning 139
Reactive Planning 139
Constraint Posting: 139
TWEAK: 140
Forward Reasoning (Chaining) 140
Hierarchical planning: 140
Least Commitment Approach 141
6.11 Exercises 142
Chapter 7 143
Perception 143
7.1 A.I. Perception 144
7.2 Perception -
7.3 Manipulation and Navigation 152
7.4 Robot architecture (Planning) 152
Deliberative Architecture 153
Reactive Architecture 154
Hybrid Architecture 154
7.5 Robot architecture (Control) 155
7.6 Summary 158
Waltz Algorithm: 158
Robot Architecture Components 158
Chapter 8 160
Natural Language Processing 160
8.1 Natural Language Processing (NLP) -
8.2 NLP – Stages in understanding 162
8.3 Syntactic Analysis -
8.4 Finite State Automata (FSA) 171
8.5 Recursive Transition Networks (RTN) 172
8.6 Augmented Transition Networks (ATN) 174
8.7 Summary 176
Syntactic Analysis : (SyA) 176
Semantic Analysis (SA) 176
Pragmatic Analysis (PA) 176
Morphological Analysis (MA) 176
Approaches in Parse Trees 177
FSA (Finite State Automaton) or FSM (Finite State Machine) 178
RTN (Recursive Transition Networks) 178
ATN (Augmented Transition Network) 179
RTN and ATN comparison 179
8.8 Exercises 180
Chapter 9 181
Artificial Neural Networks 181
9.1 Human Nervous System 182
9.2 Artificial Neural Networks -
9.3 Perceptron 187
9.4 Feed Forward Networks 189
9.5 Applications of Artificial Neural Networks 194
9.6 Applications of Artificial Neural Networks: Pattern Recognition 194
9.6 Summary 197
ANN and their comparison with biological neural networks 197
Fully connected neural network 198
Layered neural network 198
Feed forward Networks 198
Learning in Neural Network by Training 198
Applications of Neural Network (NN) 198
# ADAPTIVE FACTOR 199
9.7 Exercises 200
Chapter 10 201
Tesla Auto Pilot Technology 201
10.1 The Sensors 202
Long Range radar 202
Ultrasonic Sensors 203
Mobileye Technology 203
10.2 Deep Learning 204
10.3 Why do Neural Networks work? 205
10.4 Legal and Ethical Issues 207
10.5 The future 208
Chapter 11 209
Expert Systems 209
11.1 What are Expert Systems? 210
11.2 Architecture of Expert Systems 211
11.3 PROLOG 218
11.4 SHINE Expert System 222
Spacecraft Health Inference Engine 223
11.5 Summary 224
Expert Systems: 224
Advantage of Expert Systems: 224
Disadvantages: 225
Utilization and Functionality of Expert System 225
11.6 Exercises 225
Chapter 12 226
Miscellaneous Topics 226
12.1 Means and End Analysis: 227
12.2 AO* Algorithm 227
12.3 Hill Climbing 228
Plateau 229
Ridge: 229
Local Maxima 229
Steepest Ascent Hill Climbing 230
Summary of Problems: 231
12.4 SCRIPTS 232
12.5 Traveling Salesman Problem 235
12.6 Water Jug Problem 237
12.7 Constraint Satisfaction 239
12.8 Tower of Hanoi 241
12.9 Vision Systems 242
Chapter 13 243
Programs 243
A STAR ALGORITHM 244
Main File for A star 251
PROGRAM for WATER JUG problem 252
Eight Tile Puzzle Using BREADTH FIRST SEARCH 255
MAIN FILE FOR BREADTH FIRST SEARCH 260
Program for N QUEENS Problem 261
TIC TAC TOE Game Playing 264
Chapter 14 276
Paper: Factoring the Contribution of an Individual Member of the Coalition for the Individual 276
Motivation: 277
Shapley Value 277
Co-
Proposed Methodology 280
Efficiency Condition: 281
Discussion: 282
Conclusion: 283
Future work 284
INDEX 286
