Exploiting Symmetries in MUS Computation

Ignace Bleukx, Hélène Verhaege, Bart Bogaerts, Tias Guns

Research output: Chapter in Book/Report/Conference proceedingConference paper

2 Citations (Scopus)

Abstract

In eXplainable Constraint Solving (XCS), it is common to extract a Minimal Unsatisfiable Subset (MUS) from a set of unsatisfiable constraints. This helps explain to a user why a constraint specification does not admit a solution. Finding MUSes can be computationally expensive for highly symmetric problems, as many combinations of constraints need to be considered. In the traditional context of solving satisfaction problems, symmetry has been well studied, and effective ways to detect and exploit symmetries during the search exist. However, in the setting of finding MUSes of unsatisfiable constraint programs, symmetries are understudied. In this paper, we take inspiration from existing symmetry-handling techniques and adapt well-known MUS-computation methods to exploit symmetries in the specification, speeding-up overall computation time. Our results display a significant reduction of runtime for our adapted algorithms compared to the baseline on symmetric problems.

Original languageEnglish
Title of host publicationProceedings of the Thirty-Ninth Conference on Artificial Intelligence (AAAI)
PublisherAssociation for the Advancement of Artificial Intelligence
Pages11122-11130
Number of pages9
Volume39
Edition11
DOIs
Publication statusPublished - 2025
EventAAAA 2025 - Proceedings of the Thirty-Ninth Conference on Artificial Intelligence - Philadelphia, Pennsylvania, USA, Philadelphia, United States
Duration: 25 Feb 20254 Mar 2025
Conference number: 39
https://aaai.org/conference/aaai/aaai-25/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
ISSN (Print)2159-5399

Conference

ConferenceAAAA 2025 - Proceedings of the Thirty-Ninth Conference on Artificial Intelligence
Abbreviated titleAAAI
Country/TerritoryUnited States
CityPhiladelphia
Period25/02/254/03/25
Internet address

Bibliographical note

Publisher Copyright:
Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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