Abstract
Solving Constraint Satisfaction Problems (CSPs) by Boolean Satisfiability (SAT) requires suitable encodings for translating CSPs to equivalent SAT instances that should not only be effectively generated, but should also be efficiently processed by SAT solvers. In this paper we investigate hierarchical and hybrid encodings, focussing on two specific encodings: the representative-sparse encoding, already proposed albeit not thoroughly tested, and a new representative-order encoding. Compared to the sparse and order encodings, two widely used and efficient encodings, these representative encodings require a smaller number of variables and longer clauses to express complex CSP constraints. The paper shows that, concerning unit propagation, these encodings are incomparable. Nevertheless, the preliminary experimental results show that the new representative encodings are clearly competitive with the original sparse and order encodings, but further studies are needed to better understand the trading between fewer variables and longer clauses of the representative encodings.
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Barahona, P., Hölldobler, S., Nguyen, VH. (2014). Representative Encodings to Translate Finite CSPs into SAT. In: Simonis, H. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2014. Lecture Notes in Computer Science, vol 8451. Springer, Cham. https://doi.org/10.1007/978-3-319-07046-9_18
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DOI: https://doi.org/10.1007/978-3-319-07046-9_18
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