Abstract
Constraint Satisfaction Problems (CSPs) and Propositional Satisfiability (SAT) are two closely related frameworks used for solving hard combinatorial problems. Despite their similarities regarding the problem formulation and the basic backtracking search algorithms they use, several advanced techniques have been developed and standardized in one framework but have been rather ignored in the other. One class of such techniques includes branching heuristics for variable and value ordering. Typically, SAT heuristics are highly sophisticated while CSP ones tend to be simplistic. In this paper we study some well known SAT heuristics with the aim of transferring them to the CSP framework. Through this attempt, new CSP branching techniques are developed; exploiting information not used by most of the standard CSP heuristics. For instance such information can be the arity of the constraints and the supports of values on the constraints. Preliminary empirical results on random problems show that this unexploited information can be used to design new efficient CSP heuristics or to enhance the performance of existing ones, like dom/wdeg.
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Argyropoulos, Y., Stergiou, K. (2008). A Study of SAT-Based Branching Heuristics for the CSP. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2008. Lecture Notes in Computer Science(), vol 5138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87881-0_5
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