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1、TheDynamicsofProbabilisticStructuralRelevanceL.C.vanderGaag&J.-J.Ch.MeyerUtrechtUniversity,DepartmentofComputerScienceP.O.Box80.089,3508TBUtrecht,TheNetherlandsAbstractProbabilisticinferencewithabeliefnetworkingeneraliscomputationallyex-pensive.Sincetheconceptofst
2、ructuralrelevanceprovidesforidentifyingpartsofabeliefnetworkthatareirrelevanttoacontextofinterest,itallowsforal-leviatingtosomeextentthecomputationalburdenofinference:inferencecanberestrictedtothenetwork'srelevantpart.Thestructurallyrelevantpartofabeliefnetwork,ho
3、wever,isnotstatic.Itmaychangedynamicallyasreasoningprogresses.Weaddressthedynamicsofstructuralrelevanceandintroducetheconceptofanindependenceprojectiontocapturethesedynamics.1IntroductionComplexproblemdomainsthatarefraughtwithuncertaintyareinthefocusofat-tentionof
4、arti cial-intelligenceresearchandhavebeensoforsometimenow.Oneofthemostpromisingframeworksfordealingwithuncertaintythathaveemergedfromthisresearchistheframeworkof(Bayesian)beliefnetworks[Pearl,1988].Thisframeworkis rmlyrootedinprobabilitytheory.Itprovidesapowerfula
5、ndintuitivelyappealingformalismforrepresentingaprobabilitydistribution;informallyspeaking,abeliefnetworkconsistsofaqualitativepart,encodingadomain'svariablesandtheprobabilisticindependencesamongtheminadirectedgraph,andaquantitativepart,encodingprobabilitiesoverthe
6、sevariables.Inaddition,theframeworko ersasetofalgorithmsforprobabilisticinference.Thebelief-networkframeworkisbecomingin-creasinglypopularforbuildingknowledge-basedsystems,andmoreandmorereal-lifeapplicationsemployingtheframeworkarebeingrealised.Asapplicationsofthe
7、belief-networkframeworkgrowlarger,thenetworksinvolvedincreaseinsizeaccordingly.Forlargebeliefnetworks,probabilisticinferenceshowsatendencytobecomerathertime-consuming.SinceprobabilisticinferenceisknowntobeNP-hard[Cooper,1990],thistendencymaynotbedeniedingeneral.In
8、manyreal-lifeproblemdomains,however,reasoningwithabeliefnetworkconcentratesononlysomevariablesofinterest.Inamedicaldiagnosticapplication,forexample,them
