Abstract
In the centralized context, global constraints have been essential for the advancement of constraint reasoning. In this paper we propose to include soft global constraints in distributed constraint optimization problems (DCOPs). Looking for efficiency, we study possible decompositions of global constraints, including the use of extra variables. We extend the distributed search algorithm BnB-ADOPT + to support these representations of global constraints. In addition, we explore the relation of global constraints with soft local consistency in DCOPs, in particular for the generalized soft arc consistency (GAC) level. We include specific propagators for some well-known soft global constraints. Finally, we provide empirical results on several benchmarks.
Christian Bessiere is partially supported by the FP7-FET ICON project 284715 and by the “Agence Nationale de la Recherche” project ANR-10-BLA-0214. Patricia Gutierrez and Pedro Meseguer are partially supported by the projects TIN2009-13591-C02-02 and Generalitat de Catalunya 2009-SGR-1434. Patricia Gutierrez has an FPI scholarship BES-2008-006653.
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Bessiere, C., Gutierrez, P., Meseguer, P. (2012). Including Soft Global Constraints in DCOPs. In: Milano, M. (eds) Principles and Practice of Constraint Programming. CP 2012. Lecture Notes in Computer Science, vol 7514. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33558-7_15
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DOI: https://doi.org/10.1007/978-3-642-33558-7_15
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