A theorem-proving approach to database integrity
Foundations of deductive databases and logic programming
Solving Advanced Reasoning Tasks Using Quantified Boolean Formulas
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A consistency-based approach for belief change
Artificial Intelligence
Weakening conflicting information for iterated revision and knowledge integration
Artificial Intelligence - Special issue on logical formalizations and commonsense reasoning
On Computing Belief Change Operations using Quantified Boolean Formulas
Journal of Logic and Computation
JELIA '08 Proceedings of the 11th European conference on Logics in Artificial Intelligence
Declarative belief set merging using merging plans
PADL'11 Proceedings of the 13th international conference on Practical aspects of declarative languages
Focused most probable world computations in probabilistic logic programs
Annals of Mathematics and Artificial Intelligence
A reasoning platform based on the MI shapley inconsistency value
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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We describe COBA 2.0, an implementation of a consistency-based framework for expressing belief change, focusing here on revision and contraction, with the possible incorporation of integrity constraints. This general framework was first proposed in [1]; following a review of this work, we present COBA 2.0's high-level algorithm, work through several examples, and describe our experiments. A distinguishing feature of COBA 2.0 is that it builds on SAT-technology by using a module comprising a state-of-the-art SAT-solver for consistency checking. As well, it allows for the simultaneous specification of revision, multiple contractions, along with integrity constraints, with respect to a given knowledge base.