Artificial Intelligence.ppt

ArtificialIntelligence,Dr.PaulWagnerDepartmentofComputerScienceUniversityofWisconsinEauClaire,Messages,ArtificialIntelligence(AI)isaninterestingsub-fieldofcomputersciencethatprovidesmanycontributionstotheoverallfieldCS420,astheAIcourseatUWEC,isagoodopportunitytobegintoexploretheseissues,Outline,OverviewAITopicsKnowledgerepresentationProblemsolvingandsearchspacemanipulationPlanningLearningCommunicatingUncertaintyIntelligentagentsRoboticsAILanguagesMICSRobotContestVideo,OverviewofArtificialIntelligence,DefinitionsfourmajorcombinationsBasedonthinkingoractingBasedonactivitylikehumansorperformedinrationalway,AIDefinitions,ActingHumanlyTuringTestcomputerpassestestifahumaninterrogatoraskingwrittenquestionscandistinguishwrittenanswersfromcomputerorhumanComputerneeds:NaturallanguageprocessingKnowledgerepresentationAutomatedreasoningMachinelearning,AIDefinitions(2),TotalTuringTestincludesvideocomponent(totestsubjectsperceptualabilities)andopportunitytopassphysicalobjectstosubjectComputeralsoneeds:ComputervisionRobotics,AIDefinitions(3),ThinkingHumanlyCognitiveModelingapproachtoAIInvolvescrossoverbetweencomputerscienceandpsychologycognitivescienceAreasofinterestCognitivemodelsNeuralnetworks,AIDefinitions(4),ThinkingRationally“Lawsofthought”approachtoAIGoal:solveanyproblembasedonlogicalmanipulationProblemsDifficulttorepresentcertaintypesofknowledge(monsense,informalknowledge)DifferencebetweensolvingproblemsinprincipleandinpracticeEputationallimits,AIDefinitions(4),ActingRationally“Designarationalagent”approachtoAIAdvantagesoverlogicapproachLogicisonlyonetoolormanythatcanbeusedtodesignrationalagentScientificadvancescanprovidemoretoolsfordevelopingbetteragents,KnowledgeRepresentation,Howtorepresentinformation?Generally,weusesomesortoftree,gridornetworkOptionsOOprogramminglanguages:classes/objectsRelationaldatabasesystem:tables/rows/columnsProblemTheworldismorevaried,withmanytypesofthingstorepresent,KnowledgeRepresentation(2),AbstractObjectsSetsSentencesMeasurementsTimesWeightsGeneralizedEventsIntervalsPlacesPhysicalObjectsProcesses,KnowledgeRepresentation(3),SomethingsareverydifficulttorepresentCommonsenseSee,ProblemSolvingandSearchSpaceManipulation,ManyAlgorithmicApproachestoProblemSolvingDepth-FirstSearchBreadth-FirstSearchVariationsDepth-LimitedSearchIterativeDeepeningDepth-FirstSearchBi-directionalSearch,ProblemSolvingandSearchSpaceManipulation(2),SmarterSearchGreedybest-firstsearchA*search(combinecostsofpathsofarpluspathfromcurrentnodetogoal)Memory-boundedheuristicsearchHeuristicmeansofestimatingameasurementsuchascostofsearch,ProblemSolvingandSearchSpaceManipulation(3),IssuesAvoidingrepeatedsearchSearchingwithpartialinformation,ProblemSolvingandSearchSpaceManipulation(4),AdversarialSearchE.g.gamesandgametreesMinimaxalgorithmAlpha-Betapruning,ProblemSolvingandSearchSpaceManipulation(5),ApplicationsofProblemSolvingExpertSystemsApproximatingthefunctionalityofanabsenthumanexpertRoboticsEncounteringunexpectedobstacles,Planning,Manytypesofproblems“Blocksworld”GettingyourselffromEauClairetotheAAAIconferenceinBostonChangingaflattireCompletingallofyourprojectsattheendofthesemesterDevelopingalargesoftwareapplication,Planning(2),ApproachesState-basedsearchPartial-orderplanningPlanninggraphsIssuesTimeSchedulingResources,Learning,Definition-BuildingoncurrentknowledgebyusingexperiencetoimproveasystemVariousapproachesSupervised/unsupervised/reinforcementFormsoflearningalgorithmsInductivelogicExample:givenasetofpoint,approximatealineDecisiontree(setofquestions,actdifferentlydependingonanswer),Learning(2),IssuesComputationalLearningTheoryIntersectionoftheoreticalCS,AI,statisticsHowmanyexamplesdoyouneed?,Communicating,Majorissue-NaturallanguageprocessingManyissuesSyntaxSemanticsContextStepsPerceptionParsingAnalysisDisambiguationIncorporation,Uncertainty,MuchknowledgeisnotabsoluteBoundarybetweenknowledgeandbeliefisgrayTechniquesfordealingwithuncertaintyProbabilisticreasoningProbabilisticreasoningovertimeFuzzysets/fuzzylogicSimpledecision-making(evaluatingutility)Complexdecision-making(takingabilitytoreevaluateintoaccount)ApplicationsExpertsystems,IntelligentAgents,EverythingwevetalkedaboutcanbeviewedintermsofembeddingintelligencewithinanagentSoftwaresystemMachinewithembeddedsoftwareRobot,IntelligentAgents(2),IssuesforagentsLimitationsonmemoryPerceivingitsenvironmentWorkingwithotheragentsAffectingitsenvironment(throughactuators)ProcessesSimplebasedonrulesComplexbasedonmultiplepiecesoflogic,dealingwithuncertainty,Robotics,Fieldencompassingelementsofcomputerscience/AI,engineering,physicalsystemsIssuesManythatwevediscussed,plus:PerceptionActuationRecentsuccessesWorkerbots(e.g.floorcleaners)Intelligentnavigation(DARPAvehiclecontest)TestenvironmentsLegoMindstormsOtherrobotpackagesorcustomsystems,AILanguages,Scheme/LISPFunctionalSimpleknowledgerepresentation(list)EasytoapplyfunctionalitytorepresentedelementsPrologLogic-basedFactsandruleseasilyrepresentedBuilt-insearchengineSpecializedlanguagesRulelanguages(e.g.CLIPS)Planninglanguages(e.g.STRIPS),CS420,Springsemester,abouteveryotheryearWillbeofferedSpring2007Prerequisite:CS330(togetSchemeandPrologbackgr