Linear Programs for Measuring Inconsistency in Probabilistic Logics

Inconsistency measures help analyzing contradictory knowledge bases and resolving inconsistencies. In recent years several measures with desirable properties have been proposed, but often these measures correspond to combinatorial or nonconvex optimization problems that are hard to solve in practice. In this paper, I study a new family of inconsistency measures for probabilistic knowledge bases. All members satisfy many desirable properties and can be computed by means of convex optimization techniques. For two members, I present linear programs whose computation is barely harder than a probabilistic satisfiability test.