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Measuring inconsistency in knowledge via quasi-classical models

Hunter, A; (2002) Measuring inconsistency in knowledge via quasi-classical models. In: EIGHTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-02)/FOURTEENTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE (IAAI-02), PROCEEDINGS. (pp. 68 - 73). M I T PRESS

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Abstract

The language for describing inconsistency is underdeveloped. If a knowledgebase (a set of formulae) is inconsistent, we need more illuminating, ways to say how inconsistent it is, or to say whether one knowledgebase is "more inconsistent" than another. To address this, we provide a general characterization of inconsistency, based on quasi-classical logic (a form of paraconsistent logic with,a more expressive semantics than Belnap's four-valued logic, and unlike other paraconsistent logic, allows the connectives to appear to behave as classical connectives). We analyse inconsistent knowledge by considering the conflicts arising in the minimal quasi-classical models for that knowledge. This is used for a measure of Coherence for each knowledgebase, and for a preference ordering, called the compromise relation, over knowledgebases. In this paper, we, formalize this framework, and consider applications in managing heterogeneous sources of knowledge.

Type:Proceedings paper
Title:Measuring inconsistency in knowledge via quasi-classical models
Event:18th National Conference on Artificial Intelligence/14th Conference on Innovative Applications of Artificial Intelligence
Location:EDMONTON, CANADA
Dates:2002-07-28 - 2002-08-01
ISBN:0-262-51129-0
Keywords:INFORMATION, LOGIC
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

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