CONTENTS PART I ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE 1

Transcription

1 Preface vii Publisher s Acknowledgements xv PART I ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE 1 1 AI: HISTORY AND APPLICATIONS From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artifice Overview of AI Application Areas Artificial Intelligence A Summary Epilogue and References Exercises 33 PART II ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH 35 2 THE PREDICATE CALCULUS Introduction The Propositional Calculus The Predicate Calculus Using Inference Rules to Produce Predicate Calculus Expressions Application: A Logic-Based Financial Advisor Epilogue and References Exercises 77 xix

2 3 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH Introduction Graph Theory Strategies for State Space Search Using the State Space to Represent Reasoning with the Predicate Calculus Epilogue and References Exercises HEURISTIC SEARCH Introduction Hill Climbing and Dynamic Programming The Best-First Search Algorithm Admissibility, Monotonicity, and Informedness Using Heuristics in Games Complexity Issues Epilogue and References Exercises STOCHASTIC METHODS Introduction The Elements of Counting Elements of Probability Theory Applications of the Stochastic Methodology Bayes Theorem Epilogue and References Exercises CONTROL AND IMPLEMENTATION OF STATE SPACE SEARCH Introduction Recursion-Based Search Production Systems The Blackboard Architecture for Problem Solving Epilogue and References Exercises 220 PART III CAPTURING INTELLIGENCE: THE AI CHALLENGE KNOWLEDGE REPRESENTATION Issues in Knowledge Representation A Brief History of AI Representational Systems 228 xx

3 7.2 Conceptual Graphs: A Network Language Alternative Representations and Ontologies Agent Based and Distributed Problem Solving Epilogue and References Exercises STRONG METHOD PROBLEM SOLVING Introduction Overview of Expert System Technology Rule-Based Expert Systems Model-Based, Case Based, and Hybrid Systems Planning Epilogue and References Exercises REASONING IN UNCERTAIN SITUATIONS Introduction Logic-Based Abductive Inference Abduction: Alternatives to Logic The Stochastic Approach to Uncertainty Epilogue and References Exercises 380 PART IV MACHINE LEARNING MACHINE LEARNING: SYMBOL-BASED Introduction A Framework for Symbol-based Learning Version Space Search The ID3 Decision Tree Induction Algorithm Inductive Bias and Learnability Knowledge and Learning Unsupervised Learning Reinforcement Learning Epilogue and References Exercises MACHINE LEARNING: CONNECTIONIST Introduction Foundations for Connectionist Networks Perceptron Learning Backpropagation Learning Competitive Learning 474 xxi

4 11.5 Hebbian Coincidence Learning Attractor Networks or Memories Epilogue and References Exercises MACHINE LEARNING: GENETIC AND EMERGENT Genetic and Emergent Models of Learning The Genetic Algorithm Classifier Systems and Genetic Programming Artificial Life and Society-Based Learning Epilogue and References Exercises MACHINE LEARNING: PROBABILISTIC Stochastic and Dynamic Models of Learning Hidden Markov Models (HMMs) Dynamic Bayesian Networks and Learning Stochastic Extensions to Reinforcement Learning Epilogue and References Exercises 570 PART V ADVANCED TOPICS FOR AI PROBLEM SOLVING AUTOMATED REASONING Introduction to Weak Methods in Theorem Proving The General Problem Solver and Difference Tables Resolution Theorem Proving PROLOG and Automated Reasoning Further Issues in Automated Reasoning Epilogue and References Exercises UNDERSTANDING NATURAL LANGUAGE The Natural Language Understanding Problem Deconstructing Language: An Analysis Syntax Transition Network Parsers and Semantics Stochastic Tools for Language Understanding Natural Language Applications Epilogue and References Exercises 632 xxii

5 PART VI EPILOGUE ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY Introduction Artificial Intelligence: A Revised Definition The Science of Intelligent Systems AI: Current Challanges and Future Direstions Epilogue and References 703 Bibliography 705 Author Index 735 Subject Index 743 xxiii