Merging Locally Correct Knowledge Bases: A Preliminary Report

dc.creatorLiberatore, Paolo
dc.date2002-12-28
dc.date.accessioned2026-07-07T03:19:19Z
dc.date.available2026-07-07T03:19:19Z
dc.descriptionBelief integration methods are often aimed at deriving a single and consistent knowledge base that retains as much as possible of the knowledge bases to integrate. The rationale behind this approach is the minimal change principle: the result of the integration process should differ as less as possible from the knowledge bases to integrate. We show that this principle can be reformulated in terms of a more general model of belief revision, based on the assumption that inconsistency is due to the mistakes the knowledge bases contain. Current belief revision strategies are based on a specific kind of mistakes, which however does not include all possible ones. Some alternative possibilities are discussed.
dc.identifierhttps://arxiv.org/abs/cs/0212053
dc.identifierhttp://arxiv.org/abs/cs/0212053
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31411
dc.subjectArtificial Intelligence
dc.subjectLogic in Computer Science
dc.subjectI.2.4, F.4.1
dc.titleMerging Locally Correct Knowledge Bases: A Preliminary Report
dc.typetext

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