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Artificial Intelligence: Its Roots and Scope | p. 1 |
AI: History and Applications | p. 3 |
From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artifice | p. 3 |
Overview of AI Application Areas | p. 20 |
Artificial Intelligence A Summary | p. 30 |
Epilogue and References | p. 31 |
Exercises | p. 33 |
Artificial Intelligence as Representation and Search | p. 35 |
The Predicate Calculus | p. 45 |
Introduction | p. 45 |
The Propositional Calculus | p. 45 |
The Predicate Calculus | p. 50 |
Using Inference Rules to Produce Predicate Calculus Expressions | p. 62 |
Application: A Logic-Based Financial Advisor | p. 73 |
Epilogue and References | p. 77 |
Exercises | p. 77 |
Structures and Strategies for State Space Search | p. 79 |
Introduction | p. 79 |
Graph Theory | p. 82 |
Strategies for State Space Search | p. 93 |
Using the State Space to Represent Reasoning with the Predicate Calculus | p. 107 |
Epilogue and References | p. 121 |
Exercises | p. 121 |
Heuristic Search | p. 123 |
Introduction | p. 123 |
Hill Climbing and Dynamic Programming | p. 127 |
The Best-First Search Algorithm | p. 133 |
Admissibility, Monotonicity, and Informedness | p. 145 |
Using Heuristics in Games | p. 150 |
Complexity Issues | p. 157 |
Epilogue and References | p. 161 |
Exercises | p. 162 |
stochastic methods | p. 165 |
Introduction | p. 165 |
The Elements of Counting | p. 167 |
Elements of Probability Theory | p. 170 |
Applications of the Stochastic Methodology | p. 182 |
Bayes Theorem | p. 184 |
Epilogue and References | p. 190 |
Exercises | p. 191 |
Control and Implementation of State Space Search | p. 193 |
Introduction | p. 193 |
Recursion-Based Search | p. 194 |
Production Systems | p. 200 |
The Blackboard Architecture for Problem Solving | p. 187 |
Epilogue and References | p. 219 |
Exercises | p. 220 |
Capturing Intelligence: The AI Challenge | p. 223 |
Knowledge Representation | p. 227 |
Issues in Knowledge Representation | p. 227 |
A Brief History of AI Representational Systems | p. 228 |
Conceptual Graphs: A Network Language | p. 248 |
Alternative Representations and Ontologies | p. 258 |
Agent Based and Distributed Problem Solving | p. 265 |
Epilogue and References | p. 270 |
Exercises | p. 273 |
Strong Method Problem Solving | p. 277 |
Introduction | p. 277 |
Overview of Expert System Technology | p. 279 |
Rule-Based Expert Systems | p. 286 |
Model-Based, Case Based, and Hybrid Systems | p. 298 |
Planning | p. 314 |
Epilogue and References | p. 329 |
Exercises | p. 331 |
Reasoning in Uncertain Situations | p. 333 |
Introduction | p. 333 |
Logic-Based Abductive Inference | p. 335 |
Abduction: Alternatives to Logic | p. 350 |
The Stochastic Approach to Uncertainty | p. 363 |
Epilogue and References | p. 378 |
Exercises | p. 380 |
Machine Learning | p. 385 |
Machine Learning: Symbol-Based | p. 387 |
Introduction | p. 387 |
A Framework for Symbol-based Learning | p. 390 |
Version Space Search | p. 396 |
The ID3 Decision Tree Induction Algorithm | p. 408 |
Inductive Bias and Learnability | p. 417 |
Knowledge and Learning | p. 422 |
Unsupervised Learning | p. 433 |
Reinforcement Learning | p. 442 |
Epilogue and References | p. 449 |
Exercises | p. 450 |
Machine Learning: Connectionist | p. 453 |
Introduction | p. 453 |
Foundations for Connectionist Networks | p. 455 |
Perceptron Learning | p. 458 |
Backpropagation Learning | p. 467 |
Competitive Learning | p. 474 |
Hebbian Coincidence Learning | p. 484 |
Attractor Networks or Memories | p. 495 |
Epilogue and References | p. 505 |
Exercises 506 | |
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