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 language | English |
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Title of host publication | Proceedings of the AAAI Conference on Artificial Intelligence, AAAI-21 |
Editors | Kevin Leyton-Brown, Mausam |
Place of Publication | Palo Alto CA USA |
Publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
Pages | 3868-3876 |
Number of pages | 9 |
Volume | 35 |
Edition | 5A |
ISBN (Electronic) | 9781713835974 |
DOIs | |
Publication status | Published - 2021 |
Externally published | Yes |
Event | AAAI Conference on Artificial Intelligence 2021 - Online, United States of America Duration: 2 Feb 2021 → 9 Feb 2021 Conference number: 35th https://aaai.org/Conferences/AAAI-21/ (Website) https://ojs.aaai.org/index.php/AAAI/issue/view/395 (Proceedings) |
Conference
Conference | AAAI Conference on Artificial Intelligence 2021 |
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Abbreviated title | AAAI 2021 |
Country/Territory | United States of America |
Period | 2/02/21 → 9/02/21 |
Internet address |
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Keywords
- Constraint Optimization
- Constraint Programming