Abstract
Determining the appropriate level of local consistency to enforce on a given instance of a Constraint Satisfaction Problem (CSP) is not an easy task. However, selecting the right level may determine our ability to solve the problem. Adaptive parameterized consistency was recently proposed for binary CSPs as a strategy to dynamically select one of two local consistencies (i.e., AC and maxRPC). In this paper, we propose a similar strategy for non-binary table constraints to select between enforcing GAC and pairwise consistency. While the former strategy approximates the supports by their rank and requires that the variables domains be ordered, our technique removes those limitations. We empirically evaluate our approach on benchmark problems to establish its advantages.
This research was supported by NSF Grant No. RI-111795 and EU project ICON (FP7-284715). Woodward was supported by an NSF GRF Grant No. 1041000 and a Chateaubriand Fellowship. Experiments were conducted on the equipment of the Holland Computing Center at the University of Nebraska–Lincoln.
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Woodward, R.J., Schneider, A., Choueiry, B.Y., Bessiere, C. (2014). Adaptive Parameterized Consistency for Non-binary CSPs by Counting Supports. In: O’Sullivan, B. (eds) Principles and Practice of Constraint Programming. CP 2014. Lecture Notes in Computer Science, vol 8656. Springer, Cham. https://doi.org/10.1007/978-3-319-10428-7_54
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