Measuring and repairing inconsistency in probabilistic knowledge bases

In this paper we present a family of measures aimed at determining the amount of inconsistency in probabilistic knowledge bases. Our approach to measuring inconsistency is graded in the sense that we consider minimal adjustments in the degrees of certainty (i.e., probabilities in this paper) of the...

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Bibliographic Details
Published in:International journal of approximate reasoning 2011-09, Vol.52 (6), p.828-840
Main Author: PICADO MUINO, David
Format: Article
Language:English
Subjects:
Adjustment
Applied sciences
Approximation
Artificial intelligence
CADIAG-2
Computer science
control theory
systems
Consistency
Exact sciences and technology
Inconsistency
Knowledge bases (artificial intelligence)
Learning and adaptive systems
Maintenance
Measures of inconsistency
Probabilistic knowledge bases
Probabilistic methods
Probabilistic satisfiability
Probability theory
Repairing
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Summary:In this paper we present a family of measures aimed at determining the amount of inconsistency in probabilistic knowledge bases. Our approach to measuring inconsistency is graded in the sense that we consider minimal adjustments in the degrees of certainty (i.e., probabilities in this paper) of the statements necessary to make the knowledge base consistent. The computation of the family of measures we present here, in as much as it yields an adjustment in the probability of each statement that restores consistency, provides the modeler with possible repairs of the knowledge base. The case example that motivates our work and on which we test our approach is the knowledge base of CADIAG-2, a well-known medical expert system.
ISSN:0888-613X
1873-4731
DOI:10.1016/j.ijar.2011.02.003