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Computing Approximate Diagnoses By Using Approximate Entailment.

Authors: ten Teije, A.; van Harmelen, F.A.H.;

Computing Approximate Diagnoses By Using Approximate Entailment.

Abstract

The most widely accepted models of diagnostic reasoning are all phrased in terms of the logical consequence relations. In work in recent years, Schaerf and Cadoli have proposed efficient approximations of the classical consequence relation. The central idea of this paper is to parameterise the notion of diagnosis over different approximations of the consequence relation. This yields a number of different approximations of classical notions of diagnosis. We derive results about the relation between approximate and classical notions of diagnosis. Our results are attractive for a number of reasons. We obtain more flexible notions of diagnosis, which can be adjusted to particular situations. Furthermore, we obtain efficient anytime algorithms for computing both approximate and classical diagnoses.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average