Constraint satisfaction in logic programming
Constraint satisfaction in logic programming
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Maintaining reversible DAC for Max-CSP
Artificial Intelligence
Partition-Based Lower Bound for Max-CSP
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Meta-constraints on violations for over constrained problems
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Russian doll search for solving constraint optimization problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Range-Based Algorithm for Max-CSP
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
A Hybrid Framework for Over-Constrained Generalized
Artificial Intelligence Review
A hybrid framework for over-constrained generalized resource-constrained project scheduling problems
Artificial Intelligence Review
A Decomposition Technique for Max-CSP
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Virtual Arc consistency for weighted CSP
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
New inference rules for Max-SAT
Journal of Artificial Intelligence Research
Soft arc consistency revisited
Artificial Intelligence
Using hard constraints for representing soft constraints
CPAIOR'11 Proceedings of the 8th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
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In recent years, many works have been carried out to solve over-constrained problems, and more specifically the Maximal Constraint Satisfaction Problem (Max-CSP), where the goal is to minimize the number of constraint violations. Some lower bounds on this number of violations have been proposed in the literature.In this paper, we characterize the constraints that are ignored by the existing results, we propose new lower bounds which takes into account some of these ignored constraints and we show how these new bounds can be integrated into existing ones in order to improve the previous results.Our work also generalize the previous studies by dealing with any kind of constraints, as non binary constraints, or constraints with specific filtering algorithms. Furthermore, in order to integrate these algorithms into any constraint solver, we suggest to represent a Max-CSP as a single global constraint. This constraint can be itself included into any set of constraint. In this way, an over-constrained part of a problem can be isolated from constraints that must be necessarily satisfied.