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dc.contributor.authorJanota M.
dc.contributor.authorChow Ch.
dc.contributor.authorAraujo J.
dc.contributor.authorCodish M.
dc.contributor.authorVojtěchovský P.
dc.date.accessioned2025-01-15T17:16:53Z
dc.date.available2025-01-15T17:16:53Z
dc.date.issued2024
dc.identifierV3S-375284
dc.identifier.citationJANOTA, M., et al. SAT-Based Techniques for Lexicographically Smallest Finite Models. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence. 38th AAAI Conference on Artificial Intelligence (AAAI-24), Vancouver, 2024-02-20/2024-02-27. Menlo Park: AAAI Press, 2024. p. 8048-8056. vol. 38. ISSN 2159-5399. DOI 10.1609/aaai.v38i8.28643.
dc.identifier.isbn978-1-57735-887-9 (online)
dc.identifier.issn2159-5399 (print)
dc.identifier.issn2374-3468 (online)
dc.identifier.urihttp://hdl.handle.net/10467/120203
dc.description.abstractThis paper proposes SAT-based techniques to calculate a specific normal form of a given finite mathematical structure (model). The normal form is obtained by permuting the domain elements so that the representation of the structure is lexicographically smallest possible. Such a normal form is of interest to mathematicians as it enables easy cataloging of algebraic structures. In particular, two structures are isomorphic precisely when their normal forms are the same. This form is also natural to inspect as mathematicians have been using it routinely for many decades. We develop a novel approach where a SAT solver is used in a black-box fashion to compute the smallest representative. The approach constructs the representative gradually and searches the space of possible isomorphisms, requiring a small number of variables. However, the approach may lead to a large number of SAT calls and therefore we devise propagation techniques to reduce this number. The paper focuses on finite structures with a single binary operation (encompassing groups, semigroups, etc.). However, the approach is generalizable to arbitrary finite structures. We provide an implementation of the proposed algorithm and evaluate it on a variety of algebraic structures.eng
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherAAAI Press
dc.relation.ispartofProceedings of the 38th AAAI Conference on Artificial Intelligence
dc.rightsCreative Commons Attribution-NonCommercial (CC BY-ND) 4.0
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectCSO: Satisfiabilityeng
dc.subjectCSO: Constraint Satisfactioneng
dc.subjectCSO: Searcheng
dc.subjectSO: Heuristic Searcheng
dc.titleSAT-Based Techniques for Lexicographically Smallest Finite Modelseng
dc.typestať ve sborníkucze
dc.typeconference papereng
dc.identifier.doi10.1609/aaai.v38i8.28643
dc.relation.projectidinfo:eu-repo/grantAgreement/Ministry of Education, Youth and Sports/LL/LL1902/CZ/Powering SMT Solvers by Machine Learning/POSTMAN
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/OPJAK/CZ.02.01.01%2F00%2F22_008%2F0004590/CZ/Robotics and advanced industrial production/ROBOPROX
dc.rights.accessopenAccess
dc.identifier.wos001239938200016
dc.type.statusPeer-reviewed
dc.type.versionpublishedVersion
dc.identifier.scopus2-s2.0-85189629835
dc.relation.conference38th AAAI Conference on Artificial Intelligence (AAAI-24)


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Creative Commons Attribution-NonCommercial (CC BY-ND) 4.0
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