Cluster-based Specification Techniques in Dempster-Shafer Theory for an Evidential Intelligence Analysis of MultipleTarget Tracks (Thesis Abstract)

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dc.creatorSchubert, Johan
dc.date2003-05-16
dc.date.accessioned2026-07-07T03:19:40Z
dc.date.available2026-07-07T03:19:40Z
dc.descriptionIn Intelligence Analysis it is of vital importance to manage uncertainty. Intelligence data is almost always uncertain and incomplete, making it necessary to reason and taking decisions under uncertainty. One way to manage the uncertainty in Intelligence Analysis is Dempster-Shafer Theory. This thesis contains five results regarding multiple target tracks and intelligence specification.
dc.description4 pages, 1 figure
dc.identifierhttps://arxiv.org/abs/cs/0305018
dc.identifierhttp://arxiv.org/abs/cs/0305018
dc.identifierAI Communications 8(2) (1995) 107-110
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31556
dc.subjectArtificial Intelligence
dc.subjectNeural and Evolutionary Computing
dc.subjectI.2.3; I.5.3
dc.titleCluster-based Specification Techniques in Dempster-Shafer Theory for an Evidential Intelligence Analysis of MultipleTarget Tracks (Thesis Abstract)
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