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dc.contributor.authorSłowik, Agnieszka
dc.contributor.authorMangla, Chaitanya
dc.contributor.authorJamnik, Mateja
dc.contributor.authorHolden, Sean
dc.contributor.authorPaulson, Lawrence
dc.date.accessioned2020-07-03T23:30:25Z
dc.date.available2020-07-03T23:30:25Z
dc.date.issued2020-03-11
dc.identifier.issn2398-7340
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/307591
dc.description.abstract<jats:p>Modern theorem provers such as Vampire utilise premise selection algorithms to control the proof search explosion. Premise selection heuristics often employ an array of continuous and discrete parameters. The quality of recommended premises varies depending on the parameter assignment. In this work, we introduce a principled probabilistic framework for optimisation of a premise selection algorithm. We present results using Sumo Inference Engine (SInE) and the Archive of Formal Proofs (AFP) as a case study. Our approach can be used to optimise heuristics on large theories in minimum number of steps.</jats:p>
dc.publisherEasyChair
dc.rightsAll rights reserved
dc.titleBayesian Optimisation for Heuristic Configuration in Automated Theorem Proving
dc.typeConference Object
prism.publicationNameEPiC Series in Computing
dc.identifier.doi10.17863/CAM.54680
dcterms.dateAccepted2019-06-27
rioxxterms.versionofrecord10.29007/q91g
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.contributor.orcidSłowik, Agnieszka [0000-0001-7113-4098]
dc.contributor.orcidMangla, Chaitanya [0000-0001-7600-1681]
dc.contributor.orcidJamnik, Mateja [0000-0003-2772-2532]
dc.contributor.orcidHolden, Sean [0000-0001-7979-1148]
dc.contributor.orcidPaulson, Lawrence [0000-0003-0288-4279]
rioxxterms.typeConference Paper/Proceeding/Abstract
pubs.funder-project-idEngineering and Physical Sciences Research Council (1788755)
pubs.funder-project-idEPSRC (2119928)
pubs.conference-nameVampire 2018 and Vampire 2019. The 5th and 6th Vampire Workshops


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