An agent specific planning algorithm

Autores
Berdun, Luis Sebastian; Amandi, Analia Adriana; Campo, Marcelo Ricardo
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Planning algorithms are often applied by intelligent agents for achieving their goals. For the plan creation, this kind of algorithm uses only an initial state definition, a set of actions, and a goal; while agents also have preferences and desires that should to be taken into account. Thus, agents need to spend time analyzing each plan returned by these algorithms to find one that satisfies their preferences. In this context, we have studied an alternative in which a classical planner could be modified to accept a new conceptual parameter for a plan creation: an agent mental state composed by preferences and constraints. In this work, we present a planning algorithm that extends a partial order algorithm to deal with the agent’s preferences. In this way, our algorithm builds an adequate plan in terms of agent mental state. In this article, we introduce this algorithm and expose experimental results showing the advantages of this adaptation.
Fil: Berdun, Luis Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina
Fil: Amandi, Analia Adriana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Campo, Marcelo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina
Materia
Intelligent Agents
Planning
Agents preferences
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/280871
Acceder
id CONICETDig_e05a95f42f9dfa8001ca480e0683575d
oai_identifier_str oai:ri.conicet.gov.ar:11336/280871
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An agent specific planning algorithmBerdun, Luis SebastianAmandi, Analia AdrianaCampo, Marcelo RicardoIntelligent AgentsPlanningAgents preferenceshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Planning algorithms are often applied by intelligent agents for achieving their goals. For the plan creation, this kind of algorithm uses only an initial state definition, a set of actions, and a goal; while agents also have preferences and desires that should to be taken into account. Thus, agents need to spend time analyzing each plan returned by these algorithms to find one that satisfies their preferences. In this context, we have studied an alternative in which a classical planner could be modified to accept a new conceptual parameter for a plan creation: an agent mental state composed by preferences and constraints. In this work, we present a planning algorithm that extends a partial order algorithm to deal with the agent’s preferences. In this way, our algorithm builds an adequate plan in terms of agent mental state. In this article, we introduce this algorithm and expose experimental results showing the advantages of this adaptation.Fil: Berdun, Luis Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; ArgentinaFil: Amandi, Analia Adriana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Campo, Marcelo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; ArgentinaPergamon-Elsevier Science Ltd2012-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/280871Berdun, Luis Sebastian; Amandi, Analia Adriana; Campo, Marcelo Ricardo; An agent specific planning algorithm; Pergamon-Elsevier Science Ltd; Expert Systems with Applications; 39; 5; 4-2012; 4860-48730957-4174CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0957417411014916info:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2011.10.006info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-02-06T12:27:06Zoai:ri.conicet.gov.ar:11336/280871instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-02-06 12:27:06.547CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An agent specific planning algorithm
title An agent specific planning algorithm
spellingShingle An agent specific planning algorithm
Berdun, Luis Sebastian
Intelligent Agents
Planning
Agents preferences
title_short An agent specific planning algorithm
title_full An agent specific planning algorithm
title_fullStr An agent specific planning algorithm
title_full_unstemmed An agent specific planning algorithm
title_sort An agent specific planning algorithm
dc.creator.none.fl_str_mv Berdun, Luis Sebastian
Amandi, Analia Adriana
Campo, Marcelo Ricardo
author Berdun, Luis Sebastian
author_facet Berdun, Luis Sebastian
Amandi, Analia Adriana
Campo, Marcelo Ricardo
author_role author
author2 Amandi, Analia Adriana
Campo, Marcelo Ricardo
author2_role author
author
dc.subject.none.fl_str_mv Intelligent Agents
Planning
Agents preferences
topic Intelligent Agents
Planning
Agents preferences
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Planning algorithms are often applied by intelligent agents for achieving their goals. For the plan creation, this kind of algorithm uses only an initial state definition, a set of actions, and a goal; while agents also have preferences and desires that should to be taken into account. Thus, agents need to spend time analyzing each plan returned by these algorithms to find one that satisfies their preferences. In this context, we have studied an alternative in which a classical planner could be modified to accept a new conceptual parameter for a plan creation: an agent mental state composed by preferences and constraints. In this work, we present a planning algorithm that extends a partial order algorithm to deal with the agent’s preferences. In this way, our algorithm builds an adequate plan in terms of agent mental state. In this article, we introduce this algorithm and expose experimental results showing the advantages of this adaptation.
Fil: Berdun, Luis Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina
Fil: Amandi, Analia Adriana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Campo, Marcelo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina
description Planning algorithms are often applied by intelligent agents for achieving their goals. For the plan creation, this kind of algorithm uses only an initial state definition, a set of actions, and a goal; while agents also have preferences and desires that should to be taken into account. Thus, agents need to spend time analyzing each plan returned by these algorithms to find one that satisfies their preferences. In this context, we have studied an alternative in which a classical planner could be modified to accept a new conceptual parameter for a plan creation: an agent mental state composed by preferences and constraints. In this work, we present a planning algorithm that extends a partial order algorithm to deal with the agent’s preferences. In this way, our algorithm builds an adequate plan in terms of agent mental state. In this article, we introduce this algorithm and expose experimental results showing the advantages of this adaptation.
publishDate 2012
dc.date.none.fl_str_mv 2012-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/280871
Berdun, Luis Sebastian; Amandi, Analia Adriana; Campo, Marcelo Ricardo; An agent specific planning algorithm; Pergamon-Elsevier Science Ltd; Expert Systems with Applications; 39; 5; 4-2012; 4860-4873
0957-4174
CONICET Digital
CONICET
url http://hdl.handle.net/11336/280871
identifier_str_mv Berdun, Luis Sebastian; Amandi, Analia Adriana; Campo, Marcelo Ricardo; An agent specific planning algorithm; Pergamon-Elsevier Science Ltd; Expert Systems with Applications; 39; 5; 4-2012; 4860-4873
0957-4174
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0957417411014916
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2011.10.006
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1856403091819069440
score 13.106097

Publicaciones similares