Abstract
The weighted constraint satisfaction problem (WCSP) framework is a soft constraint framework which can model many real life optimization or over-constrained problems. While there are many local consistency notions available to speed up WCSP solving, in this paper, we investigate how to effectively combine and channel mutually redundant WCSP models to increase constraint propagation. This successful technique for reducing search space in classical constraint satisfaction has been shown non-trivial when adapted for the WCSP framework. We propose a parameterized local consistency LB(m,Φ), which can be instantiated with any local consistency Φ for single models and applied to a combined model with m sub-models, and also provide a simple algorithm to enforce it. We instantiate LB(m,Φ) with different state-of-the-art local consistencies AC*, FDAC*, and EDAC*, and demonstrate empirically the efficiency of the algorithm using different benchmark problems.
We thank the anonymous referees for their constructive comments. The work described in this paper was substantially supported by grants (CUHK4358/02E, CUHK4219/04E, and CUHK4132/07E) from the Research Grants Council of Hong Kong SAR .
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References
Larrosa, J.: Node and arc consistency in weighted CSP. In: Proc. of AAAI 2002, pp. 48–53 (2002)
Larrosa, J., Schiex, T.: Solving weighted CSP by maintaining arc consistency. Artificial Intelligence 159(1-2), 1–26 (2004)
Larrosa, J., Schiex, T.: In the quest of the best form of local consistency for weighted CSP. In: Proc. of IJCAI 2003, pp. 239–244 (2003)
de Givry, S., Heras, F., Zytnicki, M., Larrosa, J.: Existential arc consistency: Getting closer to full arc consistency in weighted CSPs. In: Proc. of IJCAI 2005, pp. 84–89 (2005)
Cheng, B.M.W., Choi, K.M.F., Lee, J.H.M., Wu, J.C.K.: Increasing constraint propagation by redundant modeling: an experience report. Constraints 4(2), 167–192 (1999)
Law, Y.C., Lee, J.H.M., Woo, M.H.C.: Speeding up weighted constraint satisfaction using redundant modeling. In: Sattar, A., Kang, B.H (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 59–68. Springer, Heidelberg (2006)
Schiex, T., Fargier, H., Verfaillie, G.: Valued constraint satisfaction problems: hard and easy problems. In: Proc. of IJCAI 1995, pp. 631–637 (1995)
Debruyne, R., Bessière, C.: Some practicable filtering techniques for the constraint satisfaction problem. In: Proc. of IJCAI 1997, pp. 412–417 (1997)
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Law, Y.C., Lee, J.H.M., Woo, M.H.C. (2007). A Parameterized Local Consistency for Redundant Modeling in Weighted CSPs . In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_21
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DOI: https://doi.org/10.1007/978-3-540-76928-6_21
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