- -

Computing programs for generalized planning using a classical planner

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Computing programs for generalized planning using a classical planner

Mostrar el registro sencillo del ítem

Ficheros en el ítem

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


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem