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A Constraint-based Approach to Learn Temporal Features on Action Models from Multiple Plans

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A Constraint-based Approach to Learn Temporal Features on Action Models from Multiple Plans

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dc.contributor.author Garrido, Antonio es_ES
dc.date.accessioned 2023-11-24T19:00:22Z
dc.date.available 2023-11-24T19:00:22Z
dc.date.issued 2022-04 es_ES
dc.identifier.issn 1383-7133 es_ES
dc.identifier.uri http://hdl.handle.net/10251/200190
dc.description.abstract [EN] Learning in AI planning tries to recognize past conducts to predict features that help improve action models. We propose a constraint programming approach for learning the temporal features, i.e., the distribution of conditions/effects and durations, of actions in an expressive temporal planning model with overlapping actions, which makes it suitable for knowledge-based multi-agent systems. We automatically build a purely declarative formulation that models time-stamps for durative actions, causal link relationships, threats and effect interferences from an arbitrary number of input plans: from just a unique single trace to many. We accommodate different degrees of input knowledge and support a different range of expressiveness, subsuming the PDDL2.1 temporal semantics. The formulation is simple but effective, and is not only valid for learning, but also for plan validation, as shown in its evaluation that returns high precision and accuracy values. es_ES
dc.description.sponsorship This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Constraints es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Learning durative action models es_ES
dc.subject Temporal planning es_ES
dc.subject Partial observability es_ES
dc.subject Constraint programming es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title A Constraint-based Approach to Learn Temporal Features on Action Models from Multiple Plans es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10601-022-09330-3 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-88476-C2-1-R/ES/RECONOCIMIENTO DE ACTIVIDADES Y PLANIFICACION AUTOMATICA PARA EL DISEÑO DE ASISTENTES INTELIGENTES/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Garrido, A. (2022). A Constraint-based Approach to Learn Temporal Features on Action Models from Multiple Plans. Constraints. 27(1-2):134-160. https://doi.org/10.1007/s10601-022-09330-3 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s10601-022-09330-3 es_ES
dc.description.upvformatpinicio 134 es_ES
dc.description.upvformatpfin 160 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 27 es_ES
dc.description.issue 1-2 es_ES
dc.relation.pasarela S\462092 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
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