Constraint acquisition and the data collection bottleneck

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dc.contributor.authorPrestwich, Steven D.
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2023-01-11T15:00:46Z
dc.date.available2023-01-11T15:00:46Z
dc.date.issued2022-02-28
dc.date.updated2023-01-11T14:45:35Z
dc.description.abstractThe field of constraint acquisition (CA) aims to remove the “modelling bottleneck” by learning constraints from examples. However, it gives rise to a “data collection bottleneck” as humans must prepare a suitable (labelled) dataset. A recently published paper described an unsupervised CA method called MineAcq that can learn standard CA benchmarks. In this paper we summarise the results, and apply MineAcq to a new, noisy, unlabelled dataset that was not designed for CA.en
dc.description.sponsorshipScience Foundation Ireland ((SFI Grant No. 12/RC/2289-P2, co-funded under the European Regional Development Fund); (SFI CONFIRM Centre for Smart Manufacturing, Research Code 16/RC/3918))en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationPrestwich, S. D. (2022) 'Constraint Acquisition and the Data Collection Bottleneck', AAAI-22: The Thirty-Sixth AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada, 22 Feb - 1 Mar.en
dc.identifier.endpage2en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/14043
dc.language.isoenen
dc.publisherAAAIen
dc.relation.ispartofAAAI-22 Workshop Program
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.urihttps://aaai.org/Conferences/AAAI-22/
dc.subjectConstraint acquisition (CA)en
dc.subjectArtificial Intelligence (AI)en
dc.subjectConstraint satisfaction problem (CSP)en
dc.subjectMineAcqen
dc.titleConstraint acquisition and the data collection bottlenecken
dc.typeConference itemen
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