Abstract
Compared to most other computational approaches to solving combinatorial problems, Constraint Programming’s distinctive feature has been its very high-level modeling primitives which expose much of the combinatorial substructures of a problem. Weighted counting on these substructures (i.e. constraints) can be used to compute beliefs about certain variable-value assignments occurring in a solution to the given constraint. A recent proposal generalizes the propagation mechanism of constraint programming to one sharing such beliefs between constraints. These beliefs, even if not computed exactly, can be very revealing for search. In this paper we investigate how best to guide combinatorial search in this cp-based belief propagation framework. We empirically evaluate branching heuristics on a wide set of benchmark constraint satisfaction problems.
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Notes
- 1.
One could argue that this is also a manifestation of inferring redundant constraints.
- 2.
Available at https://github.com/PesantGilles/MiniCPBP.
- 3.
The search guidance provided by its branching heuristics currently ignores solution cost and so it is unlikely to perform well on constraint optimization problems.
- 4.
- 5.
Available at https://www.cril.univ-artois.fr/~lecoutre/#/softwares.
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Available at http://www.minicp.org/.
- 7.
We no longer report on Dubois and PigeonPlus now that they have been settled.
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Acknowledgements
The authors wish to thank the anonymous referees for their constructive criticism that helped improve this work. Financial support for this research was provided by IVADO through the Canada First Research Excellence Fund (CFREF) grant, the Fonds de recherche du Québec–Nature et technologies (FRQNT), and NSERC Discovery Grant 218028/2017. This research was enabled in part by support provided by Calcul Québec and Compute Canada.
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Babaki, B., Omrani, B., Pesant, G. (2020). Combinatorial Search in CP-Based Iterated Belief Propagation. In: Simonis, H. (eds) Principles and Practice of Constraint Programming. CP 2020. Lecture Notes in Computer Science(), vol 12333. Springer, Cham. https://doi.org/10.1007/978-3-030-58475-7_2
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