Learning probabilistic action models from interpretation transitions

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/84962

dc.contributor	Institut de Robòtica i Informàtica Industrial
dc.contributor	Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
dc.contributor.author	Martínez Martínez, David
dc.contributor.author	Ribeiro, Tony
dc.contributor.author	Inoue, Katsumi
dc.contributor.author	Alenyà Ribas, Guillem
dc.contributor.author	Torras, Carme
dc.date	2015
dc.identifier.citation	Martínez, D., Ribeiro, T., Inoue, K., Alenyà, G., Torras, C. Learning probabilistic action models from interpretation transitions. A: Technical Communications of the International Conference on Logic Programming. "Proceedings of the Technical Communications of the 31st International Conference on Logic Programming (ICLP 2015)". Cork: 2015, p. 1-14.
dc.identifier.uri	http://hdl.handle.net/2117/84962
dc.language.iso	eng
dc.relation	http://ceur-ws.org/Vol-1433/tc_30.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject	learning (artificial intelligence)
dc.subject	planning (artificial intelligence)
dc.subject	uncertainty handling.
dc.subject	Classificació INSPEC::Cybernetics::Artificial intelligence::Learning (artificial intelligence)
dc.title	Learning probabilistic action models from interpretation transitions
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	Probabilistic planners are very flexible tools that provide good solutions for difficult tasks. However, they rely on a model of the domain and actions, which they have difficulties to 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
dc.description.abstract	Peer Reviewed

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