A Model-Theoretic Approach for Recovering Consistent Data from Inconsistent Knowledge-Bases

Abstract
One of the most signi cant drawbacks of classical logic is its being useless in the presence of an inconsistency. Nevertheless, the classical calculus is a very convenient framework to work with. In this work we propose means for drawing conclusions from systems that are based on classical logic, although the informationmightbe inconsistent. The idea is to detect those parts of the knowledge-base that \cause" the inconsistency, and isolate the parts that are \recoverable". We do this by temporarily switching into Ginsberg/Fitting multi-valued framework of bilattices (which is a common framework for logic programmingand nonmonotonic reasoning). Our method is conservative in the sense that it considers the contradictory data as useless and regards all the remaining information una ected. The resulting logic is nonmonotonic, paraconsistent, and a plausibility logic in the sense of Lehmann
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