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dc.contributor.authorMartínez Martínez, David
dc.contributor.authorAlenyà Ribas, Guillem
dc.contributor.authorTorras, Carme
dc.contributor.authorRibeiro, Tony
dc.contributor.authorInoue, Katsumi
dc.contributor.otherUniversitat Politècnica de Catalunya. Institut de Robòtica i Informàtica Industrial
dc.date.accessioned2017-04-21T11:20:20Z
dc.date.available2017-04-21T11:20:20Z
dc.date.issued2016
dc.identifier.citationMartínez, D., Alenyà, G., Torras, C., Ribeiro, T., Inoue, K. Learning relational dynamics of stochastic domains for planning. A: International Conference on Automated Planning and Scheduling. "Proceedings of the 26th International Conference on Automated Planning and Scheduling, ICAPS 2016". Londres: 2016, p. 235-243.
dc.identifier.urihttp://hdl.handle.net/2117/103612
dc.description.abstractProbabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.
dc.format.extent9 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.otherInductive logic programming (ILP)
dc.subject.otherScheduling
dc.subject.otherStochastic Systems
dc.subject.otherEndogenous growth
dc.subject.otherExperimental validations
dc.subject.otherLearning approach
dc.subject.otherOptimization method
dc.subject.otherPlanning operators
dc.subject.otherRelational representations
dc.subject.otherState transitions
dc.subject.otherStochastic domains
dc.titleLearning relational dynamics of stochastic domains for planning
dc.typeConference report
dc.contributor.groupUniversitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Optimisation::Mathematical programming::Stochastic programming
dc.subject.inspecClassificació INSPEC::Optimisation
dc.relation.publisherversionhttp://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13079
dc.rights.accessOpen Access
drac.iddocument19160630
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorMartínez, D., Alenyà, G., Torras, C., Ribeiro, T., Inoue, K.
upcommons.citation.contributorInternational Conference on Automated Planning and Scheduling
upcommons.citation.pubplaceLondres
upcommons.citation.publishedtrue
upcommons.citation.publicationNameProceedings of the 26th International Conference on Automated Planning and Scheduling, ICAPS 2016
upcommons.citation.startingPage235
upcommons.citation.endingPage243
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