AAAI Publications, Fifteenth International Conference on the Principles of Knowledge Representation and Reasoning

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Consolidating Probabilistic Knowledge Bases via Belief Contraction
Glauber De Bona, Marcelo Finger, Márcio Moretto Ribeiro, Yuri David Santos, Renata Wassermann

Last modified: 2016-03-30

Abstract


This paper is set to study the applicability of AGM-like operations to probabilistic bases. We focus on the problem of consistency restoration, also called consolidation or contraction by falsity. We aim to identify the reasons why the set of AGM postulates based on discrete operations of deletions and accretions is too coarse to treat finely adjustable probabilistic formulas. We propose new principles that allow one to deal with the consolidation of inconsistent probabilistic bases, presenting a finer method called liftable contraction. Furthermore, we show that existing methods for probabilistic consolidation via distance minimization are particular cases of the methods proposed.

Keywords


belief revision; probabilistic logic; consolidation

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