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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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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

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Título: A Constraint-based Approach to Learn Temporal Features on Action Models from Multiple Plans
Autor: Garrido, Antonio
Entidad UPV: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Fecha difusión:
Resumen:
[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 ...[+]
Palabras clave: Learning durative action models , Temporal planning , Partial observability , Constraint programming
Derechos de uso: Reserva de todos los derechos
Fuente:
Constraints. (issn: 1383-7133 )
DOI: 10.1007/s10601-022-09330-3
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/s10601-022-09330-3
Código del Proyecto:
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/
Agradecimientos:
This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R.
Tipo: Artículo

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