Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Propositional knowledge base revision and minimal change
Artificial Intelligence
On the complexity of propositional knowledge base revision, updates, and counterfactuals
Artificial Intelligence
General patterns in nonmonotonic reasoning
Handbook of logic in artificial intelligence and logic programming (vol. 3)
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
On Exact Selection of Minimally Unsatisfiable Subformulae
Annals of Mathematics and Artificial Intelligence
Tracking MUSes and Strict Inconsistent Covers
FMCAD '06 Proceedings of the Formal Methods in Computer Aided Design
Algorithms for Computing Minimal Unsatisfiable Subsets of Constraints
Journal of Automated Reasoning
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
On the measure of conflicts: Shapley Inconsistency Values
Artificial Intelligence
Approaches to measuring inconsistent information
Inconsistency Tolerance
A scalable algorithm for minimal unsatisfiable core extraction
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Towards efficient MUS extraction
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
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In this paper we show how to build a reasoning platform using an inconsistency value. The idea is to use an inconsistency value for evaluating how much each formula of the belief base is responsible of the inconsistency of the base. Then this evaluation allows us to obtain a stratification (total pre-order) of the base, that can be used as the preferential input for different reasoning tasks, such as inference, belief revision, or conciliation. We show that the obtained operators are interesting and have good logical properties. We use as inconsistency value, the MI Shapley inconsistency value, that is known to have good properties, and that can be computed from minimal inconsistent subsets. We developed a java-based platform, that use the Sat4j library for computing the minimal inconsistent subsets, and that allows to have an effective way to compute the MI Shapley inconsistent subsets. We implemented also several inference, revision and conciliation methods, that use this inconsistency value. So this provides a complete reasoning platform, that can be used for instance for academic purposes.