| 词条 | 人工智能：复杂问题求解的结构和策略 |
| 释义 | 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。 仅限于中华人民共和国境内(不包括中国香港、澳门特别行政区和中国台湾地区)销售发行。 《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 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 |
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