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1、JournalofMachineLearningResearch6(2005)1651–1678Submitted8/04;Revised6/05;Published10/05ActiveCoevolutionaryLearningofDeterministicFiniteAutomataJoshBongardJOSH.BONGARD@CORNELL.EDUHodLipsonHOD.LIPSON@CORNELL.EDUComputationalSynthesisLaboratorySibleySchoolofMechanicalandAerospaceEngineeringI
2、thaca,NY14853,USAEditor:StefanWrobelAbstractThispaperdescribesanactivelearningapproachtotheproblemofgrammaticalinference,specif-icallytheinferenceofdeterministicfiniteautomata(DFAs).Werefertothealgorithmastheestimation-explorationalgorithm(EEA).Thisapproachdiffersfrompreviouspassiveandactive
3、learningapproachestogrammaticalinferenceinthattrainingdataisactivelyproposedbytheal-gorithm,ratherthanpassivelyreceivingtrainingdatafromsomeexternalteacher.Hereweshowthatthisalgorithmoutperformsoneversionofthemostpowerfulsetofalgorithmsforgrammaticalinference,evidencedrivenstatemerging(EDSM
4、),onrandomly-generatedDFAs.TheperformanceincreaseisduetothefactthattheEDSMalgorithmonlyworkswellforDFAswithspecificbal-ances(percentageofpositivelabelings),whiletheEEAismoreconsistentoverawiderrangeofbalances.BasedonthisfindingweproposeamoregeneralmethodforgeneratingDFAstobeusedinthedevelopme
5、ntoffuturegrammaticalinferencealgorithms.Keywords:grammaticalinference,evolutionarycomputation,deterministicfiniteautomata,activelearning,systemidentification1.IntroductionGrammaticalinferenceisapopularmachinelearningdomain(refertoCicchelloandKremer,2003,foranoverview):ithaswideapplicabilityi
6、nbothcomputationallinguisticsandrelatedfields,aswellasgivingrisetoahostofbenchmarkproblems(Tomita,1982;Langetal.,1998)andcompe-titions.Grammaticalinferenceisaspecialcaseofthelargerproblemdomainofinductivelearning(BergadanoandGunetti,1995),whichaimstoconstructmodelsofsomeunderlyingsystembased
7、onsetsofpositiveandnegativeclassifications.Inoneclassofgrammaticalinferencemethods,thesystemisconsideredtobesomekindoflanguageorclassifier,andmodelsarerepresentedasdeterministicfiniteautomata(DFA).Boththetargetsystemandmodelstakestringsofsymbolsasinput(sent