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.2021.49
URN: urn:nbn:de:0030-drops-153408
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/15340/
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Senthooran, Ilankaikone ; Klapperstueck, Matthias ; Belov, Gleb ; Czauderna, Tobias ; Leo, Kevin ; Wallace, Mark ; Wybrow, Michael ; de la Banda, Maria Garcia

Human-Centred Feasibility Restoration

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LIPIcs-CP-2021-49.pdf (2 MB)

Abstract

Decision systems for solving real-world combinatorial problems must be able to report infeasibility in such a way that users can understand the reasons behind it, and understand how to modify the problem to restore feasibility. Current methods mainly focus on reporting one or more subsets of the problem constraints that cause infeasibility. Methods that also show users how to restore feasibility tend to be less flexible and/or problem-dependent. We describe a problem-independent approach to feasibility restoration that combines existing techniques from the literature in novel ways to yield meaningful, useful, practical and flexible user support. We evaluate the resulting framework on two real-world applications.

BibTeX - Entry

@InProceedings{senthooran_et_al:LIPIcs.CP.2021.49,
  author =	{Senthooran, Ilankaikone and Klapperstueck, Matthias and Belov, Gleb and Czauderna, Tobias and Leo, Kevin and Wallace, Mark and Wybrow, Michael and de la Banda, Maria Garcia},
  title =	{{Human-Centred Feasibility Restoration}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{49:1--49:18},
  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/2021/15340},
  URN =		{urn:nbn:de:0030-drops-153408},
  doi =		{10.4230/LIPIcs.CP.2021.49},
  annote =	{Keywords: Combinatorial optimisation, modelling, human-centred, conflict resolution, feasibility restoration, explainable AI, soft constraints}
}

Keywords: Combinatorial optimisation, modelling, human-centred, conflict resolution, feasibility restoration, explainable AI, soft constraints
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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