A filtering algorithm for constraints of difference in CSPs
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A New Approach to Computing Optimal Schedules for the Job-Shop Scheduling Problem
Proceedings of the 5th International IPCO Conference on Integer Programming and Combinatorial Optimization
Contradicting Conventional Wisdom in Constraint Satisfaction
PPCP '94 Proceedings of the Second International Workshop on Principles and Practice of Constraint Programming
Constraints-driven scheduling and resource assignment
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Domain filtering consistencies for non-binary constraints
Artificial Intelligence
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Domain filtering consistencies
Journal of Artificial Intelligence Research
Learning implied global constraints
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Optimal and suboptimal singleton arc consistency algorithms
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A greedy approach to establish singleton arc consistency
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Combination of among and cardinality constraints
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
A framework for decision-based consistencies
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
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This work concentrates on improving the robustness of constraint solvers by increasing the propagation strength of constraint models in a declarative and automatic manner. Our objective is to efficiently identify and remove shavable values during search. A value is shavable if as soon as it is assigned to its associated variable an inconsistency can be detected, making it possible to refute it. We extend previous work on shaving by using different techniques to decide if a given value is an interesting candidate for the shaving process. More precisely, we exploit the semantics of (global) constraints to suggest values, and reuse both the successes and failures of shaving later in search to tune shaving further. We illustrate our approach with two important global constraints, namely alldifferent and sum, and present the results of an experimentation obtained for three problem classes. The experimental results are quite encouraging: we are able to significantly reduce the number of search nodes (even by more than two orders of magnitude), and improve the average execution time by one order of magnitude.