George F.Luger 1973年在賓夕法尼亞大學(xué)獲得博士學(xué)位，并在之后的5年間在愛丁堡大學(xué)人工智能系進(jìn)行博士后研究，現(xiàn)在是新墨西哥大學(xué)計(jì)算機(jī)科學(xué)研究、語(yǔ)言學(xué)及心理學(xué)教授。
圖書目錄
Preface Publisher's Acknowledgements PART I ARTIFIClAL INTELLIGENCE：ITS ROOTS AND SCOPE 1 A1：HISTORY AND APPLICATIONS 1.1 From Eden to ENIAC：Attitudes toward Intelligence，Knowledge，andHuman Artifice 1.2 0verview ofAl Application Areas 1.3 Artificial Intelligence A Summary 1.4 Epilogue and References 1.5 Exercises PART II ARTIFlClAL INTELLIGENCE AS REPRESENTATION AN D SEARCH 2 THE PREDICATE CALCULUS 2.0 Intr0血ction 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 Introducfion 3.1 GraphTheory 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.l Hill Climbing and Dynamic Programmin9 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 ofCountin9 5.2 Elements ofProbabilityTheory 5.3 Applications ofthe 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 l93 6.1 Recursion.Based Search 6.2 Production Systems 6.3 The Blackboard Architecture for Problem Solvin9 6.4 Epilogue and References 6.5 Exercises PARTIII CAPTURING INTELLIGENCE：THE AI CHALLENGE 7 KNOWLEDGE REPRESENTATION 7.0 Issues in Knowledge Representation 7.1 A BriefHistory ofAI Representational Systems …… 8 STRONG METHOD PROBLEM SOLVING 9 REASONING IN UNCERTAIN SITUATIONS PART Ⅳ MACHINE LEARNING 10 MACHINE LEARNING:SYMBOL-BASED 11 MACHINE LEARNING:CONNECTIONIST 12 MACHINE LEARNING:GENETIC AND EMERGENT 13 MACHINE LEARNING:PROBABILISTIC PART Ⅴ ADVANCED TOPICS FOR AI PROBLEM SOLVING 14 AUTOMATED REASONING 15 UNDERSTANDING NATURAL LANGUAGE PART Ⅵ EPILOGUE 16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY
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