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一个新颖的策略用来在复杂的环境中高效谈判Anovelstrategyforefficientnegotiationincomplexenvironments
OutlineIntroductionRelatedWorkNegotiationEnvironmentEMARExperimentalAnalysisConclusion
AutomatedBilateralMulti-IssueNegotiationTwo-agentsProfitableContractMultipleIssuesConflictiveImportanceIntroduction
第三次高潮IntroductionRealisticScenariosForBilateralMulti-IssueNegotiationsAreComplexNoinformationabouttheiropponentNopriorknowledgeaboutthenegotiationdomainDeadlineanddiscountComputationalefficiency
第三次高潮IntroductionEMAR(EmpiricalModeDecompositionandAutoRegressiveMovingAverage)1.EfficientOpponentModelingPredictingtheutilitiesoftheopponent’sfuturecounter-offers2.AdaptiveConcessionMakingDynamicallyadaptingtheconcessionrate
第三次高潮RelatedWorkMainIdea:Generatingandutilizingopponentmodelsinordertooptimizeanagent’snegotiationbehaviorLearningtheopponent’spreferenceprofileLearningtheopponent’snegotiationstrategy
第三次高潮RelatedWorkLearningtheopponent’spreferenceprofileBayesianlearningtoapproximatetheopponentpreferenceprofileR.Lin,S.Kraus,J.Wilkenfeld,andJ.Barry.Negotiatingwithboundedrationalagentsinenvironmentswithincompleteinformationusinganautomatedagent.Artif.Intell.,172:823–851,April2008.KerneldensityestimationisusedasanapproximationtechniqueR.M.CoehoornandN.R.Jennings.Learningonopponent’spreferencestomakeeffectivemulti-issuenegotiationtrade-offs.InProceedingsofthe6thInt.conf.onElectroniccommerce,ICEC’04,pages59–68,NewYork,NY,USA,2004.ACM.Drawback：computationallyintractablefordomainshavingalargeoutcomespace(especiallyifreal-timeconstraintsapply)
第三次高潮RelatedWorkLearningtheopponent’snegotiationstrategyChebychevpolynomialstoestimatethechancethatthenegotiationpartneracceptsanofferinrepeatedsingle-issuenegotiationsPredictfuturecounter-offersonlineonthebasisoftheprevi
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