Cover -- Contents -- Preface -- Publisher's Acknowledgements -- PART I: ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE -- 1 AI: HISTORY AND APPLICATIONS -- 1.1 From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artifice -- 1.2 Overview of AI Application Areas -- 1.3 Artificial Intelligence-A Summary -- 1.4 Epilogue and References -- 1.5 Exercises -- PART II: ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH -- 2 THE PREDICATE CALCULUS -- 2.0 Introduction -- 2.1 The Propositional Calculus -- 2.2 The Predicate Calculus -- 2.3 Using Inference Rules to Produce Predicate Calculus Expressions -- 2.4 Application: A Logic-Based Financial Advisor -- 2.5 Epilogue and References -- 2.6 Exercises -- 3 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH -- 3.0 Introduction -- 3.1 Graph Theory -- 3.2 Strategies for State Space Search -- 3.3 Using the State Space to Represent Reasoning with the Predicate Calculus -- 3.4 Epilogue and References -- 3.5 Exercises -- 4 HEURISTIC SEARCH -- 4.0 Introduction -- 4.1 Hill Climbing and Dynamic Programming -- 4.2 The Best-First Search Algorithm -- 4.3 Admissibility, Monotonicity, and Informedness -- 4.4 Using Heuristics in Games -- 4.5 Complexity Issues -- 4.6 Epilogue and References -- 4.7 Exercises -- 5 STOCHASTIC METHODS -- 5.0 Introduction -- 5.1 The Elements of Counting -- 5.2 Elements of Probability Theory -- 5.3 Applications of the Stochastic Methodology -- 5.4 Bayes' Theorem -- 5.5 Epilogue and References -- 5.6 Exercises -- 6 CONTROL AND IMPLEMENTATION OF STATE SPACE SEARCH -- 6.0 Introduction -- 6.1 Recursion-Based Search -- 6.2 Production Systems -- 6.3 The Blackboard Architecture for Problem Solving -- 6.4 Epilogue and References -- 6.5 Exercises -- PART III: CAPTURING INTELLIGENCE: THE AI CHALLENGE -- 7 KNOWLEDGE REPRESENTATION -- 7.0 Issues in Knowledge Representation.

7.1 A Brief History of AI Representational Systems -- 7.2 Conceptual Graphs: A Network Language -- 7.3 Alternative Representations and Ontologies -- 7.4 Agent Based and Distributed Problem Solving -- 7.5 Epilogue and References -- 7.6 Exercises -- 8 STRONG METHOD PROBLEM SOLVING -- 8.0 Introduction -- 8.1 Overview of Expert System Technology -- 8.2 Rule-Based Expert Systems -- 8.3 Model-Based, Case Based, and Hybrid Systems -- 8.4 Planning -- 8.5 Epilogue and References -- 8.6 Exercises -- 9 REASONING IN UNCERTAIN SITUATIONS -- 9.0 Introduction -- 9.1 Logic-Based Abductive Inference -- 9.2 Abduction: Alternatives to Logic -- 9.3 The Stochastic Approach to Uncertainty -- 9.4 Epilogue and References -- 9.5 Exercises -- PART IV: MACHINE LEARNING -- 10 MACHINE LEARNING: SYMBOL-BASED -- 10.0 Introduction -- 10.1 A Framework for Symbol-based Learning -- 10.2 Version Space Search -- 10.3 The ID3 Decision Tree Induction Algorithm -- 10.4 Inductive Bias and Learnability -- 10.5 Knowledge and Learning -- 10.6 Unsupervised Learning -- 10.7 Reinforcement Learning -- 10.8 Epilogue and References -- 10.9 Exercises -- 11 MACHINE LEARNING: CONNECTIONIST -- 11.0 Introduction -- 11.1 Foundations for Connectionist Networks -- 11.2 Perceptron Learning -- 11.3 Backpropagation Learning -- 11.4 Competitive Learning -- 11.5 Hebbian Coincidence Learning -- 11.6 Attractor Networks or "Memories" -- 11.7 Epilogue and References -- 11.8 Exercises -- 12 MACHINE LEARNING: GENETIC AND EMERGENT -- 12.0 Genetic and Emergent Models of Learning -- 12.1 The Genetic Algorithm -- 12.2 Classifier Systems and Genetic Programming -- 12.3 Artificial Life and Society-Based Learning -- 12.4 Epilogue and References -- 12.5 Exercises -- 13 MACHINE LEARNING: PROBABILISTIC -- 13.0 Stochastic and Dynamic Models of Learning -- 13.1 Hidden Markov Models (HMMs).

13.2 Dynamic Bayesian Networks and Learning -- 13.3 Stochastic Extensions to Reinforcement Learning -- 13.4 Epilogue and References -- 13.5 Exercises -- PART V: ADVANCED TOPICS FOR AI PROBLEM SOLVING -- 14 AUTOMATED REASONING -- 14.0 Introduction to Weak Methods in Theorem Proving -- 14.1 The General Problem Solver and Difference Tables -- 14.2 Resolution Theorem Proving -- 14.3 PROLOG and Automated Reasoning -- 14.4 Further Issues in Automated Reasoning -- 14.5 Epilogue and References -- 14.6 Exercises -- 15 UNDERSTANDING NATURAL LANGUAGE -- 15.0 The Natural Language Understanding Problem -- 15.1 Deconstructing Language: An Analysis -- 15.2 Syntax -- 15.3 Transition Network Parsers and Semantics -- 15.4 Stochastic Tools for Language Understanding -- 15.5 Natural Language Applications -- 15.6 Epilogue and References -- 15.7 Exercises -- PART VI: EPILOGUE -- 16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY -- 16.0 Introduction -- 16.1 Artificial Intelligence: A Revised Definition -- 16.2 The Science of Intelligent Systems -- 16.3 AI: Current Challanges and Future Directions -- 16.4 Epilogue and References -- Bibliography -- Author Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Y -- Z -- Subject Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.