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dc.contributor.author | Segovia-Aguas, Javier | es_ES |
dc.contributor.author | Jiménez-Celorrio, Sergio | es_ES |
dc.contributor.author | Jonsson, Anders | es_ES |
dc.date.accessioned | 2020-05-29T03:32:13Z | |
dc.date.available | 2020-05-29T03:32:13Z | |
dc.date.issued | 2019-07 | es_ES |
dc.identifier.issn | 0004-3702 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/144555 | |
dc.description.abstract | [EN] Generalized planning is the task of generating a single solution (a generalized plan) that is valid for multiple planning instances. In this paper we introduce a novel formalism for representing generalized plans that borrows two mechanisms from structured programming: control flow and procedure calls. On one hand, control flow structures allow to compactly represent generalized plans. On the other hand, procedure calls allow to represent hierarchical and recursive solutions as well as to reuse existing generalized plans. The paper also presents a compilation from generalized planning into classical planning which allows us to compute generalized plans with off-the-shelf planners. The compilation can incorporate prior knowledge in the form of auxiliary procedures which expands the applicability of the approach to more challenging tasks. Experiments show that a classical planner using our compilation can compute generalized plans that solve a wide range of generalized planning tasks, including sorting lists of variable size or DFS traversing variable-size binary trees. Additionally the paper presents an extension of the compilation for computing generalized plans when generalization requires a high-level state representation that is not provided a priori. This extension brings a new landscape of benchmarks to classical planning since classification tasks can naturally be modeled as generalized planning tasks, and hence, as classical planning tasks. Finally the paper shows that the compilation can be extended to compute control knowledge for off-the-shelf planners and solve planning instances that are difficult to solve without such additional knowledge. | es_ES |
dc.description.sponsorship | Anders Jonsson is partially supported by the grants TIN2015-67959 and PCIN-2017-082 of the Spanish Ministry of Science. Sergio Jimenez is partially supported by the grants, RYC-2015-18009 and TIN2017-88476-C2-1-R of the Spanish Ministry of Science. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Artificial Intelligence | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Generalized planning | es_ES |
dc.subject | Classical planning | es_ES |
dc.subject | Planning and learning | es_ES |
dc.subject | Program synthesis | es_ES |
dc.subject.classification | CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL | es_ES |
dc.title | Computing programs for generalized planning using a classical planner | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.artint.2018.10.006 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//TIN2015-67959-P/ES/SOLUCIONADORES PARA LA INTELIGENCIA ARTIFICIAL GENERAL CON APLICACIONES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI//PCIN-2017-082/ES/LA AUTONOMIA DE LARGO PLAZO QUE EVOLUCIONA DINAMICAMENTE/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//RYC-2015-18009/ES/RYC-2015-18009/ | 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 | Cerrado | es_ES |
dc.description.bibliographicCitation | Segovia-Aguas, J.; Jiménez-Celorrio, S.; Jonsson, A. (2019). Computing programs for generalized planning using a classical planner. Artificial Intelligence. 272:52-85. https://doi.org/10.1016/j.artint.2018.10.006 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.artint.2018.10.006 | es_ES |
dc.description.upvformatpinicio | 52 | es_ES |
dc.description.upvformatpfin | 85 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 272 | es_ES |
dc.relation.pasarela | S\388672 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |