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A Decomposition Technique for Solving {Max-CSP}

Abstract : The objective of the Maximal Constraint Satisfaction Problem (Max-CSP) is to find an instantiation which minimizes the number of constraint violations in a constraint network. In this paper, inspired from the concept of inferred disjunctive constraints intro- duced by Freuder and Hubbe, we show that it is possible to exploit the arc-inconsistency counts, associated with each value of a net- work, in order to avoid exploring useless portions of the search space. The principle is to reason from the distance between the two best values in the domain of a variable, according to such counts. From this reasoning, we can build a decomposition technique which can be used throughout search in order to decompose the current prob- lem into easier sub-problems. Interestingly, this approach does not depend on the structure of the constraint graph, as it is usually pro- posed. Alternatively, we can dynamically post hard constraints that can be used locally to prune the search space. The practical interest of our approach is illustrated, using this alternative, with an experi- mentation based on a classical branch and bound algorithm, namely PFC-MRDAC.
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Submitted on : Tuesday, September 24, 2013 - 11:24:28 AM
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  • HAL Id : hal-00865285, version 1



Hachemi Bennaceur, Christophe Lecoutre, Olivier Roussel. A Decomposition Technique for Solving {Max-CSP}. 18th European Conference on Artificial Intelligence (ECAI'08), 2008, Patras, Greece. pp.500-504. ⟨hal-00865285⟩



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