Learning dynamical models using motifs

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dc.contributor.author Provan, Gregory
dc.contributor.editor Greene, Derek
dc.contributor.editor MacNamee, Brian
dc.contributor.editor Ross, Robert
dc.date.accessioned 2017-08-15T08:55:58Z
dc.date.available 2017-08-15T08:55:58Z
dc.date.issued 2016-09
dc.identifier.citation Provan, Gregory (2016) 'Learning dynamical models using motifs', in Greene, D., MacNamee, B. and Ross, R. (eds.) Proceedings of the 24th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Ireland, 20-21 September. CEUR Workshop Proceedings, 1751, pp. 161-172 en
dc.identifier.volume 1751 en
dc.identifier.startpage 161
dc.identifier.endpage 172
dc.identifier.issn 16130073
dc.identifier.uri http://hdl.handle.net/10468/4460
dc.description.abstract Automatically creating dynamical system models, M, from data is an active research area for a range of real-world applications, such as systems biology and engineering. However, the overall inference complexity increases exponentially in terms of the number of variables in M. We solve this exponential growth by using canonical representations of system motifs (building blocks) to constrain the model search during automated model generation. The motifs provide a good prior set of building blocks from which we can generate system-level models, and the canonical representation provides a theoretically sound framework for modifying the equations to improve the initial models. We present an automated method for learning dynamical models from motifs, such that the models optimize a domain-specific performance metric.We demonstrate our approach on hydraulic systems models. en
dc.description.sponsorship Science Foundation Ireland (SFI Grants 12/RC/2289 and 13/RC/2094) en
dc.description.uri http://aics2016.ucd.ie/ en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Sun SITE Central Europe / RWTH Aachen University en
dc.relation.ispartof 24th Irish Conference on Artificial Intelligence and Cognitive Science 2016
dc.relation.uri http://ceur-ws.org/Vol-1751/
dc.rights © 2016, Gregory Provan. en
dc.rights.uri http://ceur-ws.org/ en
dc.subject Model en
dc.subject Motif en
dc.subject Canonical en
dc.subject Dynamical en
dc.subject Hydraulic en
dc.title Learning dynamical models using motifs en
dc.type Conference item en
dc.internal.authorcontactother Gregory Provan, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: g.provan@cs.ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2017-08-11T11:48:08Z
dc.description.version Published Version en
dc.internal.rssid 406576068
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.identifier.journaltitle CEUR Workshop Proceedings en
dc.internal.copyrightchecked Yes en
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Dublin, Ireland en
dc.internal.IRISemailaddress g.provan@cs.ucc.ie en

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