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Efficiently reasoning with interval constraints in forward search planning

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Planificación y Aprendizajees
dc.contributor.authorColes, Amanda
dc.contributor.authorColes, Andrew
dc.contributor.authorMartínez Muñoz, Moises
dc.contributor.authorDelfa, Juan Manuel
dc.contributor.authorRosa Turbides, Tomás Eduardo de la
dc.contributor.authorEscudero Martín, Yolanda
dc.contributor.authorGarcía Olaya, Ángel
dc.contributor.funderEuropean Commissiones
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2020-05-20T09:04:29Z
dc.date.available2020-05-20T09:04:29Z
dc.date.issued2019-01-27
dc.description27 de enero - 1 de febrero 2019, Hilton Hawaiian Village, Honolulu,Hawaii, USAes
dc.description.abstractIn this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting.en
dc.description.sponsorshipThis work has received funding from the European Union’s Horizon 2020 Research and Innovation programme (Grant Agreement 730086, ERGO); EPSRC grant EP/P008410/1 (AI Planning with Continuous Non-Linear Change); the European Space Agency (ESA/ESTEC) GOTCHA project, Contract No. 4000117648/16/NL/GLC/fk; and Ministerio de Economía, Industria y Competitividad TIN2017-88476-C2-2-R and TIN2015-65686-C5.en
dc.identifier.bibliographicCitationGarcia Olaya, Angel; De La Rosa Turbides, Tomas Eduardo; Escudero Martin, Yolanda; Coles, Amanda; Coles, Andrew; Martínez, Moises; Savas, Emre; Delfa, Juan Manuel (2019). Efficiently reasoning with interval constraints in forward search planning . Proceedings of the AAAI Conference on Artificial Intelligence. Estados Unidos De America: Aaai Press. Association For The Advancement Of Artificial Intelligence . Pp. 7562-7569en
dc.identifier.isbn9781577358091
dc.identifier.issn2159-5399
dc.identifier.publicationfirstpage7562
dc.identifier.publicationissue1
dc.identifier.publicationlastpage7569
dc.identifier.publicationtitleProceedings of the AAAI Conference on Artificial Intelligenceen
dc.identifier.publicationvolume33
dc.identifier.urihttps://hdl.handle.net/10016/30448
dc.identifier.uxxiCC/0000030442
dc.language.isoengen
dc.publisherAaai Press. Association For The Advancement Of Artificial Intelligenceen
dc.relation.eventdate2019-01-27
dc.relation.eventplaceHonolulu, Hawaiien
dc.relation.eventtitleThirty-Third AAAI Conference on Artificial Intelligence Thirty-First Conference on Innovative Applications of Artificial Intelligence The Ninth Symposium on Educational Advances in Artificial Intelligenceen
dc.relation.projectIDGobierno de España. TIN2017-88476-C2-2-Res
dc.relation.projectIDGobierno de España. TIN2015-65686-C5es
dc.relation.projectIDinfo:eu-repo/grantAgreement/730086es
dc.relation.projectIDinfo:eu-repo/grantAgreement/EP/P008410/1es
dc.rightsCopyright © 2019, Association for the Advancement of Artificial Intelligenceen
dc.rights.accessRightsopen accesses
dc.subject.ecienciaInformáticaes
dc.titleEfficiently reasoning with interval constraints in forward search planningen
dc.typeconference proceedings*
dc.type.hasVersionAM*
dspace.entity.typePublication
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