Solving Inconsistencies in Probabilistic Knowledge Bases via Inconsistency Measures

摘要

In most knowledge-based systems, the guarantee of consistency is one of the essential tasks to ensure them to avoid the trivial cases. Because of this reason, a wide range of approaches has been proposed for restoring consistency. However, these approaches often correspond to logical, or probabilistic-logical framework. In this paper, we investigate a model for restoring the consistency of probabilistic knowledge bases by focusing on the method of changing the probabilities in such knowledge bases. To this aim, a process to restore the consistency based on inconsistency measures is introduced, a set of rational and intuitive axioms to characterize the restoring operators is proposed, and several logical properties are investigated and discussed.