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dc.contributor.authorAkgün, Özgür
dc.contributor.authorDang, Nguyen
dc.contributor.authorEspasa, Joan
dc.contributor.authorMiguel, Ian
dc.contributor.authorSalamon, András Z.
dc.contributor.authorStone, Christopher
dc.date.accessioned2021-07-16T10:30:15Z
dc.date.available2021-07-16T10:30:15Z
dc.date.issued2020-09-07
dc.identifier.citationAkgün , Ö , Dang , N , Espasa , J , Miguel , I , Salamon , A Z & Stone , C 2020 , Exploring instance generation for automated planning . in ModRef 2020 - The 19th workshop on Constraint Modelling and Reformulation . < http://arxiv.org/abs/2009.10156 >en
dc.identifier.otherPURE: 274776491
dc.identifier.otherPURE UUID: c651056c-471d-41e3-af7d-f7f4ca9b23ec
dc.identifier.otherArXiv: http://arxiv.org/abs/2009.10156v1
dc.identifier.otherORCID: /0000-0002-6930-2686/work/96141180
dc.identifier.otherORCID: /0000-0001-9519-938X/work/96141244
dc.identifier.otherORCID: /0000-0002-2693-6953/work/96141537
dc.identifier.otherORCID: /0000-0002-1415-9712/work/96141568
dc.identifier.urihttp://hdl.handle.net/10023/23590
dc.descriptionFunding: This work is supported by EPSRC grant EP/P015638/1. Nguyen Dang is a Leverhulme Early Career Fellow.en
dc.description.abstractMany of the core disciplines of artificial intelligence have sets of standard benchmark problems well known and widely used by the community when developing new algorithms. Constraint programming and automated planning are examples of these areas, where the behaviour of a new algorithm is measured by how it performs on these instances. Typically the efficiency of each solving method varies not only between problems, but also between instances of the same problem. Therefore, having a diverse set of instances is crucial to be able to effectively evaluate a new solving method. Current methods for automatic generation of instances for Constraint Programming problems start with a declarative model and search for instances with some desired attributes, such as hardness or size. We first explore the difficulties of adapting this approach to generate instances starting from problem specifications written in PDDL, the de-facto standard language of the automated planning community. We then propose a new approach where the whole planning problem description is modelled using Essence, an abstract modelling language that allows expressing high-level structures without committing to a particular low level representation in PDDL.
dc.format.extent17
dc.language.isoeng
dc.relation.ispartofModRef 2020 - The 19th workshop on Constraint Modelling and Reformulationen
dc.rightsCopyright © 2021 by the authors.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecom-mons.org/licenses/by/4.0/).en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectT-NDASen
dc.subject.lccQA75en
dc.titleExploring instance generation for automated planningen
dc.typeConference itemen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews.Centre for Interdisciplinary Research in Computational Algebraen
dc.identifier.urlhttps://modref.github.io/ModRefHistoryen
dc.identifier.urlhttp://arxiv.org/abs/2009.10156en


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