Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Specific Filtering Algorithms for Over-Constrained Problems
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Arc consistency for soft constraints
Artificial Intelligence
Solving weighted CSP by maintaining arc consistency
Artificial Intelligence
On global warming: Flow-based soft global constraints
Journal of Heuristics
A new local consistency for weighted CSP dedicated to long domains
Proceedings of the 2006 ACM symposium on Applied computing
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
In the quest of the best form of local consistency for weighted CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Bounds arc consistency for weighted CSPs
Journal of Artificial Intelligence Research
A new hybrid tractable class of soft constraint problems
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Hybrid tractability of valued constraint problems
Artificial Intelligence
Hierarchically nested convex VCSP
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Tractable triangles and cross-free convexity in discrete optimisation
Journal of Artificial Intelligence Research
Including soft global constraints in DCOPs
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Propagating soft table constraints
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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Powerful consistency techniques, such as AC* and FDAC*, have been developed for Weighted Constraint Satisfaction Problems (WCSPs) to reduce the space in solution search, but are restricted to only unary and binary constraints. On the other hand, van Hoeve et al. developed efficient graph-based algorithms for handling soft constraints as classical constraint optimization problems. We prove that naively incorporating van Hoeve's method into the WCSP framework can enforce a strong form of Ø-Inverse Consistency, which can prune infeasible values and deduce good lower bound estimates. We further show how Van Hoeve's method can be modified so as to handle cost projection and extension to maintain the stronger AC* and FDAC* generalized for non-binary constraints. Using the soft allDifferent constraint as a testbed, preliminary results demonstrate that our proposal gives improvements up to an order of magnitude both in terms of time and pruning.