人工智能 复杂问题求解的结构和策略 英文版 第6版

图书标准信息

• 作者 [美]卢格尔 著
• 出版社 机械工业出版社
• 出版时间 2009-03
• 版次 1
• ISBN 9787111256564
• 定价 46.00元
• 装帧 平装
• 开本 32开
• 纸张 胶版纸
• 页数 753页
• 正文语种 英语
• 原版书名 Artificial Intelligence:Structures And Strategies For Complex Problem Solving
• 丛书 经典原版书库

【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。

【作者简介】

　　GeorgeF.Luger1973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
PublishersAcknowledgements
PARTⅠARTIFIClALINTELLIGENCE：ITSROOTSANDSCOPE
1A1：HISTORYANDAPPLICATIONS
1.1FromEdentoENIAC：AttitudestowardIntelligence，Knowledge，andHumanArtifice
1.20verviewofAlApplicationAreas
1.3ArtificialIntelligenceASummary
1.4EpilogueandReferences
1.5Exercises

PARTⅡARTIFlClALINTELLIGENCEASREPRESENTATIONANDSEARCH
2THEPREDICATECALCULUS
2.0Intr0血ction
2.1ThePropositionalCalculus
2.2ThePredicateCalculus
2.3UsingInferenceRulestoProducePredicateCalculusExpressions
2.4Application：ALogic-BasedFinancialAdvisor
2.5EpilogueandReferences
2.6Exercises

3STRUCTURESANDSTRATEGIESFORSTATESPACESEARCH
3.0Introducfion
3.1GraphTheory
3.2StrategiesforStateSpaceSearch
3.3usingthestateSpacetoRepresentReasoningwiththePredicateCalculus
3.4EpilogueandReferences
3.5Exercises

4HEURISTICSEARCH
4.0Introduction
4.lHillClimbingandDynamicProgrammin9
4.2TheBest-FirstSearchAlgorithm
4.3Admissibility,Monotonicity,andInformedness
4.4UsingHeuristicsinGames
4.5ComplexityIssues
4.6EpilogueandReferences
4.7Exercises

5STOCHASTICMETHODS
5.0Introduction
5.1TheElementsofCountin9
5.2ElementsofProbabilityTheory
5.3ApplicationsoftheStochasticMethodology
5.4BayesTheorem
5.5EpilogueandReferences
5.6Exercises

6coNTROLANDIMPLEMENTATIONOFSTATESPACESEARCH
6.0Introductionl93
6.1Recursion.BasedSearch
6.2ProductionSystems
6.3TheBlackboardArchitectureforProblemSolvin9
6.4EpilogueandReferences
6.5Exercises

PARTⅢCAPTURINGINTELLIGENCE：THEAICHALLENGE
7KNOWLEDGEREPRESENTATION
7.0IssuesinKnowledgeRepresentation
7.1ABriefHistoryofAIRepresentationalSystems
7.2ConceptualGraphs：ANetworkLanguage
7.3AlternativeRepresentationsandOntologies
7.4AgentBasedandDistributedProblemSolving
7.5EpilogueandReferences
7.6Exercises

8STRONGMETHODPROBLEMSOLVING
8.0Introduction
8.1OverviewofExpertSygemTechnology
8.2Rule.BasedExpertSygems
8.3Model-Based,CaseBasedandHybridSystems
8.4Planning
8.5EpilogueandReferences
8.6Exercises
9REASONINGINUNCERTAINSTUATIONS
9.0Introduction
9.1Logic-BasedAbductiveInference
9.2Abduction：AlternativestoLogic
9.3TheStochasticApproachtoUncertainty
9.4EpilogueandReferences
9.5Exercises

PARTⅣ
MACHINELEARNING
10MACHINELEARNING：SYMBOL-BASED
10.0Introduction
10.1AFrameworkforSymbolbasedLearning
10.2versionSpaceSearch
10.3TheID3DecisionTreeInductionAlgorithm
10.4InductiveBiasandLearnability
10.5KnowledgeandLearning
10.6UnsupervisedLearning
10.7ReinforcementLearning
10.8EpilogueandReferenees
10.9Exercises

11MACHINELEARNING：CONNECTIONtST
11.0Introduction
11.1FoundationsforConnectionistNetworks
11.2PerceptronLearning
11.3BackpropagationLearning
11.4CompetitiveLearning
11.5HebbianCoincidenceLearning
11.6AttractorNetworksor“Memories”
11.7EpilogueandReferences
11.8Exercises506

12MACHINELEARNING：GENETICANDEMERGENT
12.0GeneticandEmergentMedeIsofLearning
12.111IcGeneticAlgorithm
12.2ClassifierSystemsandGeneticProgramming
12.3ArtmcialLifeandSociety-BasedLearning
12.4EpilogueandReferences
12.5Exercises

13MACHINELEARNING：PROBABILISTIC
13.0StochasticandDynamicModelsofLearning
13.1HiddenMarkovModels(HMMs)
13.2DynamicBayesianNetworksandLearning
13.3StochasticExtensionstoReinforcementLearning
13.4EpilogueandReferences
13.5Exercises

PARTⅤ
AD，ANCEDTOPlCSFORAlPROBLEMSOLVING
14AUTOMATEDREASONING
14.0IntroductiontoWeakMethodsinTheoremProving
14.1TIIeGeneralProblemSolverandDifiel"enceTables
14.2ResolutionTheOremProving
14.3PROLOGandAutomatedReasoning
14.4FurtherIssuesinAutomatedReasoning
14.5EpilogueandReferences
14.6Exercises

15UNDERs-rANDINGNATURALLANGUAGE
15.0TheNaturalLang~~geUnderstandingProblem
15.1DeconstructingLanguage：AnAnalysis
15.2Syntax
15.3TransitionNetworkParsersandSemantics
15.4StochasticToolsforLanguageUnderstanding
15.5NaturalLanguageApplications
15.6EpilogueandReferences
15.7Exercises
……
PARTⅥEPILOGUE
16ARTIFICIALINTELLIGENCEASEMPIRICALENQUIRY