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1、ProductionMatchingforLargeLearningSystemsRobertB.DoorenbosJanuary31,1995CMU-CS-95-113ComputerScienceDepartmentCarnegieMellonUniversityPittsburgh,PASubmittedinpartialful llmentoftherequirementsforthedegreeofDoctorofPhilosophy.ThesisCommittee:JillFainLehma
2、n,ComputerScienceDepartmentTomMitchell,ComputerScienceDepartmentManuelaVeloso,ComputerScienceDepartmentPaulRosenbloom,UniversityofSouthernCalifornia/InformationSciencesInstitutec1995RobertB.DoorenbosThisresearchwassponsoredinpartbyaNationalScienceFoundat
3、ionGraduateFellowship,andbytheWrightLaboratory,AeronauticalSystemsCenter,AirForceMaterielCommand,USAF,andtheAdvancedResearchProjectsAgency(ARPA)undergrantnumberF33615-93-1-1330.Viewsandconclusionscontainedinthisdocumentarethoseoftheauthorandshouldnotbein
4、terpretedasnecessarilyrepresentingocialpoliciesorendorsements,eitherexpressedorimplied,ofWrightLaboratoryortheUnitedStatesGovernment.TheU.S.GovernmentisauthorizedtoreproduceanddistributereprintsforGovernmentalpurposes,notwithstandinganycopyrightnotation
5、thereon.Keywords:arti cialintelligence,large-scaleproductionsystems,machinelearning,produc-tionmatch,SoarAbstractOneofthecentralresultsofAIresearchinthe1970'swasthattoachievegoodperformance,AIsystemsmusthavelargeamountsofknowledge.Machinelearningtechniqu
6、eshavebeendevelopedtoautomaticallyacquireknowledge,oftenintheformofif-thenrules(productions).Unfortunately,thisoftenleadstoautilityproblem
7、thelearning"endsupcausinganoverallslowdowninthesystem.Thisisbecausethemorerulesasystemhas,thelongerittakestomatcht
8、hemagainstthecurrentsituationinordertodeterminewhichonesareapplicable.Toaddressthisproblem,thisthesisisaimedatenablingthescalingupofthenumberofrulesinproductionsystems.Weexamineadiversesetoftestbedsystems,eachofwhichlearnsatleast100,000rules.Weshowthatwi
9、ththebestexistingmatchalgorithms,thematchcostincreaseslinearlyinthenumberofrulesinthesesystems.Thisisinadequateforlargelearningsystems,becauseitleadstoautilityproblem.Wethenexaminethecausesofthislinearincrease,anddevelopte
