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dc.contributor.authorKelsey, Tom
dc.contributor.authorKotthoff, Lars
dc.contributor.authorJefferson, Christopher Anthony
dc.contributor.authorLinton, Stephen Alexander
dc.contributor.authorMiguel, Ian James
dc.contributor.authorNightingale, Peter
dc.contributor.authorGent, Ian Philip
dc.date.accessioned2019-03-27T12:30:05Z
dc.date.available2019-03-27T12:30:05Z
dc.date.issued2014-04
dc.identifier.citationKelsey , T , Kotthoff , L , Jefferson , C A , Linton , S A , Miguel , I J , Nightingale , P & Gent , I P 2014 , ' Qualitative modelling via constraint programming ' Constraints , vol. 19 , no. 2 , pp. 163-173 . https://doi.org/10.1007/s10601-014-9158-6en
dc.identifier.issn1383-7133
dc.identifier.otherPURE: 105502246
dc.identifier.otherPURE UUID: 59db640e-cc1a-43fc-b7e6-dd3c2703391d
dc.identifier.otherScopus: 84898818731
dc.identifier.otherORCID: /0000-0002-5052-8634/work/34029955
dc.identifier.otherORCID: /0000-0002-8091-1458/work/27201541
dc.identifier.otherWOS: 000334052400005
dc.identifier.urihttp://hdl.handle.net/10023/17386
dc.descriptionThis work was supported by United Kingdom EPSRC grant EP/H004092/1. LK was supported by EU FP7 grants 284715 (ICON) and 288147 (ePolicy).en
dc.description.abstractQualitative modelling is a technique integrating the fields of theoretical computer science, artificial intelligence and the physical and biological sciences. The aim is to be able to model the behaviour of systems without estimating parameter values and fixing the exact quantitative dynamics. Traditional applications are the study of the dynamics of physical and biological systems at a higher level of abstraction than that obtained by estimation of numerical parameter values for a fixed quantitative model. Qualitative modelling has been studied and implemented to varying degrees of sophistication in Petri nets, process calculi and constraint programming. In this paper we reflect on the strengths and weaknesses of existing frameworks, we demonstrate how recent advances in constraint programming can be leveraged to produce high quality qualitative models, and we describe the advances in theory and technology that would be needed to make constraint programming the best option for scientific investigation in the broadest sense.
dc.language.isoeng
dc.relation.ispartofConstraintsen
dc.rights© 2014, Springer Science+Business Media New York. This work has been made available online in accordance with the publisher's policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1007/s10601-014-9158-6en
dc.subjectConstraint programmingen
dc.subjectQualitative modelsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectArtificial Intelligenceen
dc.subject.lccQA75en
dc.titleQualitative modelling via constraint programmingen
dc.typeJournal articleen
dc.description.versionPostprinten
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.doihttps://doi.org/10.1007/s10601-014-9158-6
dc.description.statusPeer revieweden


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