We investigate an approach for drawing logical inference from inconsistent premisses. The main idea in this approach is that the inconsistencies in the premisses should be interpreted as uncertainty of the information. We propose a mechanism, based on Kinght’s [14] study of inconsistency, for revising an inconsistent set of premisses to a minimally uncertain, probabilistically consistent one. We will then generalise the probabilistic entailment relation introduced in [15] for propositional languages to the first order case to draw logical inference from (...) a probabilistic set of premisses. We will show how this combination can allow us to limit the effect of uncertainty introduced by inconsistent premisses to only the reasoning on the part of the premise set that is relevant to the inconsistency. (shrink)

An important issue in the management of knowledge-based systems is the handling of inconsistency. This problem has recently been attracting a lot of attention from Artificial Intelligence community. When inconsistency occurs in a knowledge base, there are mainly two ways to deal with it; we either resolve it or accept inconsistency and cope with it. This paper tackles the problem of evaluating the amount of contradiction in propositional knowledge bases, and provides a new measure of conflict based on deductive argumentation (...) theory. Measuring the degree of conflict of a knowledge base can help us to deal with inconsistencies. Several semantic- and syntax-based approaches have been proposed separately. Given the pivotal role of argumentation in representing and handling inconsistency, in this paper, we use logical argumentation as a way to compute the inconsistency measure for propositional formulae. We show using the complete argumentation tree that our family of inconsistency measures is able to... (shrink)

Branching time logics have been studied in computer science since the 1980s primarily to model the tree of computations for discrete transition systems. Inconsistency measures for propositional log...

In this paper, we ask: how should an agent who has incoherent credences update when they learn new evidence? The standard Bayesian answer for coherent agents is that they should conditionalize; however, this updating rule is not defined for incoherent starting credences. We show how one of the main arguments for conditionalization, the Dutch strategy argument, can be extended to devise a target property for updating plans that can apply to them regardless of whether the agent starts out with coherent (...) or incoherent credences. The main idea behind this extension is that the agent should avoid updating plans that increase the possible sure loss from Dutch strategies. This happens to be equivalent to avoiding updating plans that increase incoherence according to a distance-based incoherence measure. (shrink)