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適性化多代理人網際網路環境資訊偵搜CollaborativeMultiagentAdaptationforBusinessEnvironmentalScanningthroughtheInternetOutlineIntroductionUCRES:User-centered,Continuous,andResource-boundedESGoal&clallengesFindingIOIcontinuouslyTimeliness(TES):minimizingtheaveragetimedelayoffindingIOICompleteness(CES):maximizingthepercentageofIOIfoundControllingtheresourceconsumedEffectiveness(EES):maximizingthepossibilityoffindingIOIineachinquiryofdataConsideringthepreferencesoftheuser(i.e.addingimportanceweightstoTES,CES,andEES)WeightedTESWeightedCESWeightedEESRelatedmultiagenttechnologyforUCRESInformationgatheringagentsAimingtosatisfyusers'"one-shot"needs,e.g.Intelligentlylocatingtheinformationwiththeadditionalconsiderationoftime/cost/qualitytradeoffs,andFilteroutirrelevantinformationButwhenandhowfrequentlytoscanforIOI?InformationmonitoringagentsAimingtomonitorapredefinedsetoftargetsperiodicallyoradaptivelyButwheretofindtheIOItomonitor?AdaptiveagentsfordecisionsupportAimingtoprovidetailoredinformationforsupportingdecisionmakingAdaptingtowhattomonitorbyobservingtheuser’spreferenceand/orproblemsolvingstrategiesButwhenandhowfrequentlytoscanforIOI?Multiagentcoordination&biddingAimingtoresolveconflictsandbuildconsensusamongtheagentsButhowtodefineacoordinationand/orbiddingprotocolforUCRES?Learningformultiagentcoordination,bidding,andorganizationAimingtolearnHowtheactionsaffecteachother,Whatinformationisrequiredforcoordination,Whentotriggeragentcoordination,Usageofwhattheagentsarebiddingfor,RestructuringoftheorganizationforcoordinationandcollaborationButhowtosimultaneouslyadapttoUser’spreferenceIOI’sdistributionintheInternetUpdatebehavioroftheIOILimitedamountofresourceMultiagentAdaptationforUCRESOverviewofAESABehaviorofeachScanningAgentBehavioroftheControllingAgentExperimentSimulatingusers’dynamicpreferencesThesystemsevaluatedAESABasicsettingForthecontrollingagent:1=2=20,=1inquirypersecond,=1inquiryper200secondsVariantsAESA-1Forthescanningagents,=0.5,=0.5,
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