License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.CP.2022.38
URN: urn:nbn:de:0030-drops-166670
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/16667/
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Ulrich-Oltean, Felix ; Nightingale, Peter ; Walker, James Alfred

Selecting SAT Encodings for Pseudo-Boolean and Linear Integer Constraints

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LIPIcs-CP-2022-38.pdf (0.8 MB)

Abstract

Many constraint satisfaction and optimisation problems can be solved effectively by encoding them as instances of the Boolean Satisfiability problem (SAT). However, even the simplest types of constraints have many encodings in the literature with widely varying performance, and the problem of selecting suitable encodings for a given problem instance is not trivial. We explore the problem of selecting encodings for pseudo-Boolean and linear constraints using a supervised machine learning approach. We show that it is possible to select encodings effectively using a standard set of features for constraint problems; however we obtain better performance with a new set of features specifically designed for the pseudo-Boolean and linear constraints. In fact, we achieve good results when selecting encodings for unseen problem classes. Our results compare favourably to AutoFolio when using the same feature set. We discuss the relative importance of instance features to the task of selecting the best encodings, and compare several variations of the machine learning method.

BibTeX - Entry

@InProceedings{ulricholtean_et_al:LIPIcs.CP.2022.38,
  author =	{Ulrich-Oltean, Felix and Nightingale, Peter and Walker, James Alfred},
  title =	{{Selecting SAT Encodings for Pseudo-Boolean and Linear Integer Constraints}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{38:1--38:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/16667},
  URN =		{urn:nbn:de:0030-drops-166670},
  doi =		{10.4230/LIPIcs.CP.2022.38},
  annote =	{Keywords: Constraint programming, SAT encodings, machine learning, global constraints, pseudo-Boolean constraints, linear constraints}
}

Keywords: Constraint programming, SAT encodings, machine learning, global constraints, pseudo-Boolean constraints, linear constraints
Collection: 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)
Issue Date: 2022
Date of publication: 23.07.2022
Supplementary Material: Dataset (Repository with models and results): https://github.com/felixvuo/cp2022-pbli-sat

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