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dc.contributor.authorKaplan, L. M.
dc.contributor.authorŞensoy, Murat
dc.contributor.authorTang, Y.
dc.contributor.authorChakraborty, S.
dc.contributor.authorBisdikian, C.
dc.contributor.authorde Mel, G.
dc.date.accessioned2016-02-15T07:33:15Z
dc.date.available2016-02-15T07:33:15Z
dc.date.issued2013
dc.identifier.isbn978-605-86311-1-3
dc.identifier.urihttp://hdl.handle.net/10679/2155
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6641238
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractThis work develops alternatives to the classical subjective logic deduction operator. Given antecedent and consequent propositions, the new operators form opinions of the consequent that match the variance of the consequent posterior distribution given opinions on the antecedent and the conditional rules connecting the antecedent with the consequent. As a result, the uncertainty of the consequent actually map to the spread for the probability projection of the opinion. Monte Carlo simulations demonstrate this connection for the new operators. Finally, the work uses Monte Carlo simulations to evaluate the quality of fusing opinions from multiple agents before and after deduction.
dc.description.sponsorshipthe U.S. Army Research Laboratory ; the U.K Ministry of Defense
dc.language.isoengen_US
dc.publisherIEEE
dc.relation.ispartofInformation Fusion (FUSION), 2013 16th International Conference on
dc.rightsrestrictedAccess
dc.titleReasoning under uncertainty: variations of subjective logic deductionen_US
dc.typeConference paperen_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0001-8806-4508 & YÖK ID 41438) Şensoy, Murat
dc.contributor.ozuauthorŞensoy, Murat
dc.identifier.startpage1910
dc.identifier.endpage1917
dc.identifier.wosWOS:000341370000254
dc.subject.keywordsMonte Carlo methods
dc.subject.keywordsOntologies (artificial intelligence)
dc.subject.keywordsProbabilistic logic
dc.identifier.scopusSCOPUS:2-s2.0-84890837332
dc.contributor.authorMale1
dc.relation.publicationcategoryConference Paper - International - Institutional Academic Staff


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