这是一本经典的人工智能教材，已被宾夕法尼亚州立大学、南加州大学、马里兰大学、杜克大学、布朗大学、乔治梅森大学等多所**大学采用为人工智能课程的指定教材。书中从人工智能（AI）的历史及其应用开始介绍，涵盖了AI问题求解的研究工具、AI和知识密集型问题求解的表示法、机器学习、重要的AI应用领域、AI编程语言LISP和PROLOG等方面的内容，*后提到了智能系统科学的可能性问题，考虑了当前AI面临的挑战，讨论了目前AI的局限，并设计了AI的未来。本书中的算法用类Pascal的伪代码描述，清晰易读。

PrefacePARTIARTIFICIALINTELLIGENCE:ITSROOTSANDSCOPE11Al:HISTORYANDAPPLICATIONS31.1FromEdentoENIAC:AttitudestowardIntelligence,Knowledge,andHumanArtifice31.2OverviewofAIApplicationAreas171.3ArtificialIntelligence--ASummary281.4EpilogueandReferences291.5Exercises31PARTIIARTIFICIALINTELLIGENCEASREPRESENTATIONANDSEARCH332THEPREDICATECALCULUS472.0Introduction472.1ThePropositionalCalculus472.2ThePredicateCalculus522.3UsingInferenceRulestoProducePredicateCalculusExpressions642.4Application:ALogic-BasedFinancialAdvisor752.5EpilogueandReferences792.6Exercises79PARTII(continued)3STRUCTURESANDSTRATEGIESFORSTATESPACESEARCH3.0Introduction813.1GraphTheory843.2StrategiesforStateSpaceSearch933.3UsingtheStateSpacetoRepresentReasoningwiththePredicateCalculus3.4EpilogueandReferences1213.5Exercises1214HEURISTICSEARCH1234.0Introduction1234.1AnAlgorithmforHeuristicSearch1274.2Admissibility,Monotonicity,andInformedness1394.3UsingHeuristicsinGames1444.4ComplexityIssues1524.5EpilogueandReferences1564.6Exercises1565CONTROLANDIMPLEMENTATIONOFSTATESPACESEARCH5.0Introduction1595.1Recursion-BasedSearch1605.2Pattern-DirectedSearch1645.3ProductionSystems1715.4TheBlackboardArchitectureforProblemSolving1875.5EpilogueandReferences1895.6Exercises190PARTIIIREPRESENTATIONANDINTELLIGENCE:THEAlCHALLENGE1936KNOWLEDGEREPRESENTATION1976.0IssuesinKnowledgeRepresentation1976.1ABriefHistoryofAIRepresentationalSystems1986.2ConceptualGraphs:ANetworkLanguage2186.3AlternativestoExplicitRepresentation2286.4AgentBasedandDistributedProblemSolving2356.5EpilogueandReferences2406.6Exercises243PARTIII(continued)7STRONGMETHODPROBLEMSOLVING2477.0Introduction2477.1OverviewofExpertSystemTechnology2497.2Rule-BasedExpertSystems2567.3Model-Based,CaseBased,andHybridSystems2687.4Planning2847.5EpilogueandReferences2997.6Exercises3018REASONINGINUNCERTAINSITUATIONS3038.0Introduction3038.1Logic-BasedAbductiveInference3058.2Abduction:AlternativestoLogic3208.3TheStochasticApproachtoUncertainty3338.4EpilogueandReferences3448.5Exercises346PARTIVMACHINELEARNING3499MACHINELEARNING:SYMBOL-BASED3519.0Introduction6039.1AFrameworkforSymbol-basedLearning3549.2VersionSpaceSearch3609.3TheID3DecisionTreeInductionAlgorithm3729.4InductiveBiasandLearnability3819.5KnowledgeandLearning3869.6UnsupervisedLearning3979.7ReinforcementLearning4069.8EpilogueandReferences4139.9Exercises41410MACHINELEARNING:CONNECTIONIST41710.0Introduction41710.1FoundationsforConnectionistNetworks41910.2PerceptronLearning42210.3BackpropagationLearning43110.4CompetitiveLearning43810.5HebbianCoincidenceLearning44610.6AttractorNetworksor"Memories"45710.7EpilogueandReferences46710.8Exercises468PARTIV(continued)11MACHINELEARNING:SOCIALANDEMERGENT46911.0SocialandEmergentModelsofLearning46911.1TheGeneticAlgorithm47111.2ClassifierSystemsandGeneticProgramming48111.3ArtificialLifeandSociety-BasedLearning49211.4EpilogueandReferences50311.5Exercises504PARTVADVANCEDTOPICSFORAlPROBLEMSOLVING50712AUTOMATEDREASONING50912.0IntroductiontoWeakMethodsinTheoremProving50912.1TheGeneralProblemSolverandDifferenceTables51012.2ResohtionTheoremProving51612.3PROLOGandAutomatedReasoning53712.4FurtherIssuesinAutomatedReasoning54312.5EpilogueandReferences55012.6Exercises55113UNDERSTANDINGNATURALLANGUAGE55313.0RoleofKnowledgeinLanguageUnderstanding55313.1DeconstructingLanguage:ASymbolicAnalysis55613.2Syntax55913.3SyntaxandKnowledgewithATNParsers56813.4StochasticToolsforLanguageAnalysis57813.5NaturalLanguageApplications58513.6EpilogueandReferences59213.7Exercises557PARTVILANGUAGESANDPROGRAMMINGTECHNIQUESFORARTIFICIALINTELLIGENCE59714ANINTRODUCTIONTOPROLOG60314.0Introduction60314.1SyntaxforPredicateCalculusProgramming60414.2AbstractDataTypes(ADTs)inPROLOG61614.3AProductionSystemExampleinPROLOG620PARTVI:14ANINTRODUCTIONTOPROLOG(continued)14.4DesigningAlternativeSearchStrategies62514.**PROLOGPlanner63014.6PROLOG:Meta-Predicates,Types,andUnification63314.7Meta-InterpretersinPROLOG641t4.8LearningAlgorithmsinPROLOG65614.9NaturalLanguageProcessinginPROLOG66614.10EpilogueandReferences67314.11Exercises6761**NINTRODUCTIONTOLISP67915.0Introduction67915.1LISP:ABriefOverview68015.2SearchinLISP:AFunctionalApproachtotheFarmer,Wolf,Goat,andCabbageProblem70215.3Higher-OrderFunctionsandProceduralAbstraction70715.4SearchStrategiesinLISP71115.5PatternMatchinginLISP71515.6ARecursiveUnificationFunction71715.7InterpretersandEmbeddedLanguages72115.8LogicProgramminginLISP72315.9StreamsandDelayedEvaluation73215.1**nExpertSystemShellinLISP73615.11SemanticNetworksandInheritanceinLISP74315.12Object-OrientedProgrammingUsingCLOS74715.13LearninginLISP:TheID3Algorithm75915.14EpilogueandReferences77115.15Exercises772PARTVIIEPILOGUE77716ARTIFICIALINTELLIGENCEASEMPIRICALENQUIRY77916.0Introduction77916.1ArtificialIntelligence:ARevisedDefinition78116.2TheScienceofIntelligentSystems79216.3Al:CurrentIssuesandFutureDirections80316.4EpilogueandReferences807Bibliography809AuthorIndex837SubjectIndex843

这是一本经典的人工智能教材，已被宾夕法尼亚州立大学、南加州大学、马里兰大学、杜克大学、布朗大学、乔治梅森大学等多所**大学采用为人工智能课程的指定教材。 书中从人工智能(AI)的历史及其应用开始介绍，涵盖了AI问题求解的研究工具、AI和知识密集型问题求解的表示法、机器学习、重要的AI应用领域、AI编程语盲LISP和PROLOG等方面的内容，*后提到了智能系统科学的可能性问题，考虑了当前AI面临的挑战，讨论了目前AI的局限，并设计了AI的未来。 本书中的算法用类Pascal的伪代码描述，清晰易读。 阅读本书要求学生已经学过离散数学课程，包括谓词演算和图论概论，并且学过数据结构课程，包括树、图、递归搜索，会使用堆栈、队列和优先队列。