Automatic generation of high-level state features for generalized planning

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• dc.contributor.author Lotinac, Damirca
• dc.contributor.author Segovia-Aguas, Javierca
• dc.contributor.author Jiménez, Sergioca
• dc.contributor.author Jonsson, Anders, 1973-ca
• dc.date.accessioned 2016-07-21T08:16:53Z
• dc.date.available 2016-07-21T08:16:53Z
• dc.date.issued 2016ca
• dc.description.abstract In many domains generalized plans can only/nbe computed if certain high-level state features,/ni.e. features that capture key concepts to accurately/ndistinguish between states and make good decisions,/nare available. In most applications of generalized/nplanning such features are hand-coded by/nan expert. This paper presents a novel method/nto automatically generate high-level state features/nfor solving a generalized planning problem. Our/nmethod extends a compilation of generalized planning/ninto classical planning and integrates the computation/nof generalized plans with the computation/nof features, in the form of conjunctive queries. Experiments/nshow that we generate features for diverse/ngeneralized planning problems and hence,/ncompute generalized plans without providing a/nprior high-level representation of the states. We/nalso bring a new landscape of challenging benchmarks/nto classical planning since our compilation/nnaturally models classification tasks as classical/nplanning problems.
• dc.description.sponsorship This work is partially supported by grant TIN2015-67959 and the/nMaria de Maeztu Units of Excellence Programme MDM-2015-/n0502, MEC, Spain. Sergio Jimenez is partially supported by the /nJuan de la Cierva program funded by the Spanish government.
• dc.format.mimetype application/pdfca
• dc.identifier.citation Lotinac D, Segovia-Aguas J, Jiménez S, Jonsson A. Automatic generation of high-level state features for generalized planning. In: Kambhampati S, editor. Proceedings of the 25th International Joint Conference on Artificial Intelligence; 2016 July 9-15; New York, United States. Palo Alto: AAAI Press; 2016. p. 3199-3205.ca
• dc.identifier.uri http://hdl.handle.net/10230/27096
• dc.language.iso engca
• dc.publisher Association for the Advancement of Artificial Intelligence (AAAI)ca
• dc.relation.ispartof Proceedings of the 25th International Joint Conference on Artificial Intelligence; 2016 July 9-15; New York, United States. Palo Alto: AAAI Press; 2016. p. 3199-3205.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-67959
• dc.rights © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org)ca
• dc.rights.accessRights info:eu-repo/semantics/openAccessca
• dc.title Automatic generation of high-level state features for generalized planningca
• dc.type info:eu-repo/semantics/conferenceObjectca
• dc.type.version info:eu-repo/semantics/acceptedVersionca