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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