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.2021.54
URN: urn:nbn:de:0030-drops-153452
URL: https://drops.dagstuhl.de/opus/volltexte/2021/15345/
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Tsouros, Dimosthenis C. ; Stergiou, Kostas

Learning Max-CSPs via Active Constraint Acquisition

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LIPIcs-CP-2021-54.pdf (0.6 MB)


Abstract

Constraint acquisition can assist non-expert users to model their problems as constraint networks. In active constraint acquisition, this is achieved through an interaction between the learner, who posts examples, and the user who classifies them as solutions or not. Although there has been recent progress in active constraint acquisition, the focus has only been on learning satisfaction problems with hard constraints. In this paper, we deal with the problem of learning soft constraints in optimization problems via active constraint acquisition, specifically in the context of the Max-CSP. Towards this, we first introduce a new type of queries in the context of constraint acquisition, namely partial preference queries, and then we present a novel algorithm for learning soft constraints in Max-CSPs, using such queries. We also give some experimental results.

BibTeX - Entry

@InProceedings{tsouros_et_al:LIPIcs.CP.2021.54,
  author =	{Tsouros, Dimosthenis C. and Stergiou, Kostas},
  title =	{{Learning Max-CSPs via Active Constraint Acquisition}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{54:1--54:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/15345},
  URN =		{urn:nbn:de:0030-drops-153452},
  doi =		{10.4230/LIPIcs.CP.2021.54},
  annote =	{Keywords: Constraint acquisition, modeling, learning}
}

Keywords: Constraint acquisition, modeling, learning
Collection: 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)
Issue Date: 2021
Date of publication: 15.10.2021


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