标题：人工智能：复杂问题求解的结构和策略
图书信息
出版社: 机械工业出版社; 第1版 (2009年3月1日)
丛书名: 经典原版书库
平装: 753页
正文语种: 英语
开本: 32
ISBN: 9787111256564
条形码: 9787111256564
尺寸: 20.8 x 14.6 x 3.2 cm
重量: 721 g
作者简介
作者：(美国)卢格尔 (Luger.G.F) 
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。
内容简介
《人工智能:复杂问题求解的结构和策略(英文版)(第6版)》英文影印版由PearsonEducationAsiaLtd授权机械工业出版社独家出版。未经出版者书面许可，不得以任何方式复制或抄袭《人工智能:复杂问题求解的结构和策略(英文版)(第6版)》内容。
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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)》封面贴有PearsonEducation(培生教育出版集团)激光防伪标签，无标签者不得销售。
媒体评论
“在该领域里学生经常遇到许罗很难的概念，通过深刻的实例与简单明了的祝圈，该书清晰而准确垲阚述了这些概念。”
——Toseph Lewis，圣迭戈州立大学
“本书是人工智能课程的完美补充。它既给读者以历史的现点，又给幽所有莰术的宾用指南。这是一本必须要推荐的人工智能的田书。”
——-Pascal Rebreyend，瑞典达拉那大学
“该书的写作风格和全面的论述使它成为人工智能领域很有价值的文献。”
——Malachy Eaton，利默里克大学[1]
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 
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 ofExpert Sygem Technology 
8.2 Rule.Based Expert Sygems 
8.3 Model-Based,Case Based and Hybrid Systems 
8.4 Planning 
8.5 Epilogue and References 
8.6 Exercises 
9 REASONING IN UNCERTAIN STUATIONS 
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 Referenees 
10.9 Exercises
11 MACHINE LEARNING：CONNECTIONtST 
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 506
12 MACHINE LEARNING：GENETIC AND EMERGENT 
12.0 Genetic and Emergent MedeIs ofLearning 
12.1 11Ic Genetic Algorithm 
12.2 Classifier Systems and Genetic Programming 
12.3 Artmcial Life and Society-Based Learning 
12.4 EpilogueandReferences 
12.5 Exercises 
13 MACHINE LEARNING：PROBABILISTIC 
13.0 Stochastic andDynamicModelsofLearning 
13.1 Hidden Markov Models(HMMs)
13.2 DynamicBayesianNetworksandLearning 
13.3 Stochastic Extensions to Reinforcement Learning
13.4 EpilogueandReferences 
13.5 Exercises 
PART Ⅴ
AD，ANCED TOPlCS FOR Al PROBLEM SOLVING 
14 AUTOMATED REASONING 
14.0 Introduction to Weak Methods inTheorem Proving 
14.1 TIIeGeneralProblem SolverandDifiel"enceTables 
14.2 Resolution TheOrem Proving 
14.3 PROLOG and Automated Reasoning 
14.4 Further Issues in Automated Reasoning 
14.5 EpilogueandReferences 
14.6 Exercises 
15 UNDERs-rANDING NATURAL LANGUAGE 
15.0 TheNaturalLang~~geUnderstandingProblem 
15.1 Deconstructing Language：An Analysis 
15.2 Syntax 
15.3 TransitionNetworkParsers and Semantics 
15.4 StochasticTools forLanguage Understanding
15.5 Natural LanguageApplications 
15.6 Epilogue and References 
15.7 Exercises 
……
PART Ⅵ EPILOGUE
16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY