作 者:拉塞尔(StuartJ.Russell) 著作
定 价:158
出 版 社:清华大学出版社
出版日期:2011年07月01日
页 数:1132
装 帧:平装
ISBN:9787302252955
●IArtifiIntelligence1Introduction1.1WhatIsAI?1.2TheFoundationsofArtifiIntelligence1.3TheHistoryofArtifiIntelligence1.4TheStateoftheArt1.5Summary,BibliographicalandHistoricalNotes,Exercises2IntelligentAgents2.1AgentsandEnvironments2.2GoodBehavior:TheConceptofRationality2.3TheNatureofEnvironments2.4TheStructureofAgents2.5Summary,BibliographicalandHistoricalNotes,ExercisesIIProblem-solving3SolvingProblemsbySearching3.1Problem-SolvingAgents3.2ExampleProblemsr3.3SearchingforSolutions3.4UninformedSearchStrategies3.5Informed(Heuristic)SearchStrategies3.6HeuristicFunctions3.7Summary,BibliographicalandHistoricalNotes,Exercises4BeyondClassicalSearch4.1LocalSearchAlgorithmsandOptimizationProblems4.2LocalSearchinContinuousSpaces4.3SearchingwithNondeterministicActions4.4SearchingwithPartialObservations4.5OnlineSearchAgentsandUnknownEnvironments4.6Summary,BibliographicalandHistoricalNotes,Exercises5AdversariaiSearch5.1Games5.2OptimalDecisionsinGames5.3Alpha-BetaPruning5.4ImperfectReal-TimeDecisions5.5StochasticGames5.6PartiallyObservableGames5.7State-of-the-ArtGamePrograms5.8AlternativeApproaches5.9Summary,BibliographicalandHistoricalNotes,Exercises6ConstraintSatisfactionProblems6.1DefiningConstraintSatisfactionProblems6.2ConstraintPropagation:InferenceinCSPs6.3BacktrackingSearchforCSPs6.4LocalSearchforCSPs6.5TheStructureofProblems6.6Summary,BibliographicalandHistoricalNotes,ExercisesIIIKnowledge,reasoning,andplanning7LogicalAgents7.1Knowledge-BasedAgents7.2TheWumpusWorld7.3Logic7.4ProitionalLogic:AVerySimpleLogic7.5ProitionalTheoremProving7.6EffectiveProitionalModelChecking7.7AgentsBasedonProitionalLogic7.8Summary,BibliographicalandHistoricalNotes,Exercises8First-OrderLogic8.1RepresentationRevisited8.2SyntaxandSemanticsofFirst-OrderLogic8.3UsingFirst-OrderLogic.8.4KnowledgeEngineeringinFirst-OrderLogic8.5Summary,BibliographicalandHistoricalNotes,Exercises9InferenceinFirst-OrderLogic9.1Proitionalvs.First-OrderInference9.2UnificationandLifting9.3ForwardChaining9.4BackwardChaining9.5Resolution9.6Summary,BibliographicalandHistoricalNotes,Exer-cises10ClassicalPlanning10.1DefinitionofClassicalPlanning10.2AlgorithmsforPlanningasState-SpaceSearch10.3PlanningGraphs10.4OtherClassicalPlanningApproaches10.5AnalysisofPlanningApproaches10.6Summary,BibliographicalandHistoricalNotes,Exercises11PlanningandActingintheRealWorld11.1Time,.Schedules,andResources11.2HierarchicalPlanning11.3PlanningandActinginNondeterministicDomains11.4ltiagentPlanning11.5Summary,BibliographicalandHistoricalNotes,Exercises12KnowledgeRepresentation12.1OntologicalEngineering12.2CategoriesandObjects12.3Events12.4MentalEventsandMent.alObjects12.5ReasoningSystemsforCategories12.6ReasoningwithDefaultInformation12.7TheInternetShoppingWorld12.8Summary,BibliographicalandHistoricalNotes,ExercisesIVUncertainknowledgeandreasoning13QuantifyingUncertainty13.1ActingunderUncertainty13.2BasicProbabilityNotation13.3InferenceUsingFullJointDistributions13.4Independence13.5Bayes'RuleandItsUse13.6TheWumpusWorldRevisited13.7Summary,BibliographicalandHistoricalNotes,Exercises14ProbabilisticReasoning14.1RepresentingKnowledgeinanUncertainDomain14.2TheSemanticsofBayesianNetworks14.3EfficientRepresentationofConditionalDistributions14.4ExactInferenceinBayesianNetworks14.5AppromateInferenceinBayesianNetworks14.6RelationalandFirst-OrderProbabilityModels14.7OtherApproachestoUncertainReasOning14.8Summary,BibliographicalandHistoricalNotes,Exercises15ProbabilisticReasoningoverTime15.1TimeandUncertainty15.2InferenceinTemporalModels15.3HiddenMarkovModels15.4KalmanFilters15.5DynamicBayesianNetworks15.6KeepingTrackofManyObjects15.7Summary,BibliographicalandHistoricalNotes,Exercises16MakingSimpleDecisions16.1CombiningBeliefsandDesiresunderUncertainty16.2TheBasisofUtilityTheory16.3UtilityFunctions16.4ltiattributeUtilityFunctions16.5DecisionNetworks16.6TheValueofInformation16.7Decision-TheoreticExpertSystems16.8Summary,BibliographicalandHistoricalNotes,Exercises17MakingComplexDecisions17.1SequentialDecisionProblems17.2ValueIteration17.3PolicyIteration17.4PartiallyObservableMDPs17.5DecisionswithltipleAgents:GameTheory17.6MechanismDesign17.7Summary,BibliographicalandHistoricalNotes,ExercisesVLearning18LearningfromExamples18.1FormsofLearning18.2SupervisedLearning18.3LearningDecisionTrees18.4EvaluatingandChoosingtheBestHypothesis18.5TheTheoryofLearning18.6Regressionand:ClassificationwithLinearModels18.7ArtifiNeuralNetworks18.8NonparametricModels18.9SupportVectorMachines18.10EnsembleLearning18.I1PracticalMachineLearning18.12Summary,BibliographicalandHistoricalNotes,Exercises19KnowledgeinLearning19.1ALogicalForlationofLearning19.2KnowledgeinLearning19.3Explanation-BasedLearning19.4LearningUsingRelevanceInformation19.5InductiveLogicProgramming19.6Summary,BibliographicalandHistoricalNotes,Exercises20LearningProbabilisticModels20:1StatisticalLearning20.2LearningwithComplete'Data20.3LearningwithHiddenVariables:TheEMAlgorithm20.4Summary,BibliographicalandHistoricalNotes,Exercises21ReinforcementLearning21.1Introduction21.2PassiveReinforcementLearning21.3ActiveReinforcementLearning21.4GeneralizationinReinforcementLearning21.5PolicySearcti21.6ApplicationsofReinforcementLearning21.7Summary,BibliographicalandHistoricalNotes,ExercisesVIComnicating,perceiving,andacting22NaturalLanguagePi'ocessing22.1LanguageModels22.2TextClassification22.3InformationRetrieval22.4InformationExtraction22.5Summary,BibliographicalandHistoricalNotes,Exercises23NaturalLanguageforComnication23.1PhraseStructureGrammars23.2SyntacticAnalysis(Parsing)23.3AugmentedGrammarsandSemanticInterpretation23.4MachineTranslation23.5SpeechRecognition23.6Summary,BibliographicalandHistoricalNotes,Exercises24Perception24.1ImageFormation24.2EarlyImage-ProcessingOperations24.3ObjectRecognitionbyAppearance24.4Reconstructingthe3DWorld24.5ObjectRecognitionfromStructuralInformation24.6.UsingVision24.7Summary,BibliographicalandHistiaricalNotes,Exercises25Robotics25.1Introduction25.2RobotHardware25.3RoboticPerception25.4PlanningtoMove25.5PlanningUncertainMovements25.6Moving25.7RoboticSoftwareArchitectures25.8ApplicationDomains.25.9Summary,BibliographicalandHistoricalNotes,ExercisesVIIConclusions26PhilosophicalFoundations26.1WeakAI:CanMachinesActIntelligently?26.2StrongAI:CanMachinesReallyThink?26.3TheEthicsandRisksofDevelopingArtifiIntelligence26.4Summary,BibliographicalandHistoricalNotes,Exercises27AI:ThePresentandFuture27.1AgentComponents27.2AgentArchitectures27.3AreWeGoingintheRightDirection?27.4WhatIfAIDoesSucceed?AMathematicalbackgroundA.1CompletyAnalysisandO0NotationA.2Vectors,Matrices,andLinearAlgebraA.3ProbabilityDistributionsBNotesonLanguagesandAlgorithmsB.1DefiningLanguageswithBackus-NaurForm(BNF)B.2DescribingAlgorithmswithPseudocodeB.3OnlineHelpBibliographyIndex
《人工智能(一种现代的方法第3版影印版》(作者拉塞尔、诺维格)是*、*经典的人工智能教材，已被全世界100多个国家的1200多所大学用作教材。 《人工智能(一种现代的方法第3版影印版》的*新版全面而系统地介绍了人工智能的理论和实践，阐述了人工智能领域的核心内容，并深入介绍了各个主要的研究方向。全书仍分为八大部分：**部分“人工智能”，第二部分“问题求解”，第三部分“知识与推理”，第四部分“规划”，第五部分“不确定知识与推理”，第六部分“学习”，第七部分“通信、感知与行动”，第八部分“结论”。《人工智能(一种现代的方法第3版影印版》既详细介绍了人工智能的基本概念、思想和算法，还描述了其各个研究方向*前沿的进展，同时收集整理了详实的历史文献与事件。另外，《人工智能(一种现代的方法第3版影印版》的配套网等
