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.2022.8
URN: urn:nbn:de:0030-drops-166373
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Berden, Senne ; Kumar, Mohit ; Kolb, Samuel ; Guns, Tias

Learning MAX-SAT Models from Examples Using Genetic Algorithms and Knowledge Compilation

LIPIcs-CP-2022-8.pdf (0.9 MB)


Many real-world problems can be effectively solved by means of combinatorial optimization. However, appropriate models to give to a solver are not always available, and sometimes must be learned from historical data. Although some research has been done in this area, the task of learning (weighted partial) MAX-SAT models has not received much attention thus far, even though such models can be used in many real-world applications. Furthermore, most existing work is limited to learning models from non-contextual data, where instances are labeled as solutions and non-solutions, but without any specification of the contexts in which those labels apply. A recent approach named hassle-sls has addressed these limitations: it can jointly learn hard constraints and weighted soft constraints from labeled contextual examples. However, it is hindered by long runtimes, as evaluating even a single candidate MAX-SAT model requires solving as many models as there are contexts in the training data, which quickly becomes highly expensive when the size of the model increases. In this work, we address these runtime issues. To this end, we make two contributions. First, we propose a faster model evaluation procedure that makes use of knowledge compilation. Second, we propose a genetic algorithm named hassle-gen that decreases the number of evaluations needed to find good models. We experimentally show that both contributions improve on the state of the art by speeding up learning, which in turn allows higher-quality MAX-SAT models to be found within a given learning time budget.

BibTeX - Entry

  author =	{Berden, Senne and Kumar, Mohit and Kolb, Samuel and Guns, Tias},
  title =	{{Learning MAX-SAT Models from Examples Using Genetic Algorithms and Knowledge Compilation}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{8:1--8:17},
  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 =		{},
  URN =		{urn:nbn:de:0030-drops-166373},
  doi =		{10.4230/LIPIcs.CP.2022.8},
  annote =	{Keywords: Machine learning, constraint learning, MAX-SAT}

Keywords: Machine learning, constraint learning, MAX-SAT
Collection: 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)
Issue Date: 2022
Date of publication: 23.07.2022
Supplementary Material: Software (Source Code): archived at:

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