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人工智能复杂问题求解的结构和策略英文版第6版
作者：（美）卢格尔 著
出版社：机械工业出版社
出版时间：2009-03-01
ISBN：9787111256564
定价：¥46.00
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内容简介
本书是一本经典的人工智能教材，全面阐述了人工智能的基础理论，有效结合了求解智能问题的数据结构以及实现的算法，把人工智能的应用程序应用于实际环境中，并从社会和哲学、心理学以及神经生理学角度对人工智能进行了独特的讨论。
作者简介
George F.Luger 1973年在宾夕法尼亚大学获得博士学位，并在之后的5年间在爱丁堡大学人工智能系进行博士后研究，现在是新墨西哥大学计算机科学研究、语言学及心理学教授。
目录
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
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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