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1、LearningNestedAgentModelsinanInformationEconomyJoseM.VidalandEdmundH.DurfeeArti cialIntelligenceLaboratoryUniversityofMichigan1101BealAvenueAnnArbor,MI48109-2110313-763-9074,313-936-1563fjmvidal,durfeeg@umich.eduMay26,1997RunningHead:LearningNestedAgentModels.AbstractWepresentourappro
2、achtotheproblemofhowanagent,withinaneconomicMulti-AgentSystem,candeterminewhenitshouldbehavestrategically(i.e.learnandusemodelsofotheragents),andwhenitshouldactasasimpleprice-taker.Weprovideaframeworkfortheincrementalimplementationofmodelingcapabilitiesinagents,andadescriptionoftheform
3、sofknowledgerequired.Theagentswereimplementedanddi erentpopulationssimulatedinordertolearnmoreabouttheirbehaviorandthemeritsofusingandlearningagentmodels.Ourresultsshow,amongotherlessons,howsavvybuyerscanavoidbeingcheated"bysellers,howpricevolatilitycanbeusedtoquantitativelypredictthe
4、bene tsofdeepermodels,andhowspeci ctypesofagentpopulationsin
uencesystembehavior.1IntroductionInopen,multi-agentsystems,agentscancomeandgowithoutanycentralcontrolorguidance,andthushowandwhichagentsinteractwitheachotherwillchangedynamically.Agentsmighttrytomanipulatetheinteractionstothe
5、irindivid-ualbene ts,atthecostoftheglobaleciency.Toavoidthis,theprotocolsandmechanismsthattheagentsengageinmightbeconstructedtomakemanipulationirrational(RosenscheinandZlotkin,1994),butunfortunatelythisstrategyisonlyapplicableinrestricteddomains.Bysituatingagentsinaneconomicsociety,as
6、wedointheUniversityofMichiganDigitalLibrary(UMDL),wecanmakeeachagentresponsibleformakingitsowndecisionsaboutwhentobuy/sellandwhotodobusinesswith(Atkinsetal.,1996).Amarket-basedinfrastructure,builtaroundcomputationalauctionagents,servestodiscourageagentsfromengaginginstrate-gicreasoning
7、tomanipulatethesystembykeepingthecompetitivepressureshigh.However,sincemanyinstancescanarisewhereimperfectionsincompetitioncouldbeexploited,agentsmightbene tfromstrategicreasoning,eitherbymanipulat-ingthesystemornotallowingotherstomanipulatethem.Butstrategicreasoningrequirese ort.Ana
