Computational methods for database repair by signed formulae*
Annals of Mathematics and Artificial Intelligence
COBA 2.0: A Consistency-Based Belief Change System
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Belief change based on global minimisation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Characterising equilibrium logic and nested logic programs: Reductions and complexity1,2
Theory and Practice of Logic Programming
Towards implementations for advanced equivalence checking in answer-set programming
ICLP'05 Proceedings of the 21st international conference on Logic Programming
A computational approach for belief change
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
A general framework for computing maximal contractions
Frontiers of Computer Science: Selected Publications from Chinese Universities
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In this paper, we show how an approach to belief revision and belief contraction can be axiomatized by means of quantified Boolean formulas. Specifically, we consider the approach of belief change scenarios, a general framework that has been introduced for expressing different forms of belief change. The essential idea is that for a belief change scenario (K, R, C), the set of formulas K, representing the knowledge base, is modified so that the sets of formulas R and C are respectively true in, and consistent with the result. By restricting the form of a belief change scenario, one obtains specific belief change operators including belief revision, contraction, update, and merging. For both the general approach and for specific operators, we give a quantified Boolean formula such that satisfying truth assignments to the free variables correspond to belief change extensions in the original approach. Hence, we reduce the problem of determining the results of a belief change operation to that of satisfiability. This approach has several benefits. First, it furnishes an axiomatic specification of belief change with respect to belief change scenarios. This then leads to further insight into the belief change framework. Second, this axiomatization allows us to identify strict complexity bounds for the considered reasoning tasks. Third, we have implemented these different forms of belief change by means of existing solvers for quantified Boolean formulas. As well, it appears that this approach may be straightforwardly applied to other specific approaches to belief change.