Towards more practical and efficient automatic dominance breaking

Jimmy H.M. Lee, Allen Z. Zhong

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)

Abstract

Dominance breaking is shown to be an effective technique to improve the solving speed of Constraint Optimization Problems (COPs). The paper proposes separate techniques to generalize and make more efficient the nogood generation phase of an automated dominance breaking framework by Lee and Zhong’s. The first contribution is in giving conditions that allow skipping the checking of non-efficiently checkable constraints and yet still produce sufficient useful nogoods, thus opening up possibilities to apply the technique on COPs that were previously impractical. The second contribution identifies and avoids the generation of dominance breaking nogoods that are both logically and propagation redundant. The nogood generation model is strengthened using the notion of Common Assignment Elimination to avoid generation of nogoods that are subsumed by other nogoods, thus reducing the search space substantially. Extensive experimentation confirms the benefits of the new proposals.

Original languageEnglish
Title of host publicationProceedings of the AAAI Conference on Artificial Intelligence, AAAI-21
EditorsKevin Leyton-Brown, Mausam
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages3868-3876
Number of pages9
Volume35
Edition5A
ISBN (Electronic)9781713835974
DOIs
Publication statusPublished - 2021
Externally publishedYes
EventAAAI Conference on Artificial Intelligence 2021 - Online, United States of America
Duration: 2 Feb 20219 Feb 2021
Conference number: 35th
https://aaai.org/Conferences/AAAI-21/ (Website)
https://ojs.aaai.org/index.php/AAAI/issue/view/395 (Proceedings)

Conference

ConferenceAAAI Conference on Artificial Intelligence 2021
Abbreviated titleAAAI 2021
Country/TerritoryUnited States of America
Period2/02/219/02/21
Internet address

Keywords

  • Constraint Optimization
  • Constraint Programming

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