SAT instances generation using Graph Variational Autoencoders

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dc.contributor.authorCrowley, Danielen
dc.contributor.authorDalla, Marcoen
dc.contributor.authorO'Sullivan, Barryen
dc.contributor.authorVisentin, Andreaen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-10-30T16:04:19Z
dc.date.available2025-10-30T16:04:19Z
dc.date.issued2024-10-11en
dc.description.abstractThis paper presents a SAT instance generator using a Graph Variational Autoencoder (GVAE2SAT) architecture that outperforms existing generative deep learning models in speed and requires minimal post-processing. Our computational analyses benchmark this model against current deep learning techniques, introducing advanced metrics for more accurate evaluation. This new model is unique in its ability to maintain partial satisfiability of SAT instances while significantly reducing computational time. Although no method perfectly addresses all challenges in generating SAT instances, our approach marks a significant step forward in the efficiency and effectiveness of SAT instance generation.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationCrowley, D., Dalla, M., O’Sullivan, B. and Visentin, A. (2024) 'SAT instances generation using graph variational autoencoders', ESANN 2024: Bruges,Belgium, 9-11 October 2024, pp. 369–374. https://doi.org/10.14428/esann/2024.ES2024-223en
dc.identifier.doi10.14428/esann/2024.ES2024-223en
dc.identifier.endpage374en
dc.identifier.isbn978-2-87587-090-2en
dc.identifier.startpage369en
dc.identifier.urihttps://hdl.handle.net/10468/18136
dc.language.isoenen
dc.publisherESANNen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme::Phase 2/12/RC/2289_P2/IE/INSIGHT_Phase 2 /en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Centres for Research Training (CRT) Programme/18/CRT/6223/IE/SFI Centre for Research Training in Artificial Intelligence/en
dc.relation.urihttps://www.esann.org/proceedings/2024en
dc.relation.urihttp://www.i6doc.com/en/en
dc.rights© 2024, the Authors.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectSATen
dc.subjectGraph Variational Autoencoderen
dc.subjectGVAE2SATen
dc.titleSAT instances generation using Graph Variational Autoencodersen
dc.typeConference itemen
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