Agents That Learn What Argument to Select In Argumentation-Based Negotiations

Autores
Monteserin, Ariel José; Amandi, Analia Adriana
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Argument selection is considered the essence of the strategy in argumentation-based negotiation. An agent, which is arguing during a negotiation, has to decide what arguments are the best to persuade the opponent. In fact, in each negotiation step, the agent must select an argument from a set of candidate arguments by applying some selection criterion. For this task, the agent observes some factors of the negotiation context, for instance trust in the opponent, expected utility, among others. Usually, argument selection mechanisms are defined statically. However, as the negotiation context varies from a negotiation to another, defining a static selection mechanism it is not useful. For this reason, we present in this paper a novel approach to personalize argument selection mechanisms in the context of argumentation-based negotiation. The selection mechanism defines a set of preferences that determine how preferable it is to utter an argument in a given context. Our approach maintains a hierarchy of preferences in order to learn new preferences and update the existing ones as the agent experience increases. We tested this approach in a simulated multiagent system and obtained promising results.
Fil: Monteserin, Ariel José. 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: Amandi, Analia Adriana. 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
Materia
ARGUMENT SELECTION
ARGUMENTATION-BASED NEGOTIATION
AUTONOMOUS AGENTS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/243715
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Agents That Learn What Argument to Select In Argumentation-Based NegotiationsMonteserin, Ariel JoséAmandi, Analia AdrianaARGUMENT SELECTIONARGUMENTATION-BASED NEGOTIATIONAUTONOMOUS AGENTShttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Argument selection is considered the essence of the strategy in argumentation-based negotiation. An agent, which is arguing during a negotiation, has to decide what arguments are the best to persuade the opponent. In fact, in each negotiation step, the agent must select an argument from a set of candidate arguments by applying some selection criterion. For this task, the agent observes some factors of the negotiation context, for instance trust in the opponent, expected utility, among others. Usually, argument selection mechanisms are defined statically. However, as the negotiation context varies from a negotiation to another, defining a static selection mechanism it is not useful. For this reason, we present in this paper a novel approach to personalize argument selection mechanisms in the context of argumentation-based negotiation. The selection mechanism defines a set of preferences that determine how preferable it is to utter an argument in a given context. Our approach maintains a hierarchy of preferences in order to learn new preferences and update the existing ones as the agent experience increases. We tested this approach in a simulated multiagent system and obtained promising results.Fil: Monteserin, Ariel José. 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: Amandi, Analia Adriana. 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; ArgentinaInternational Association for the Development of the Information Society2010-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/243715Monteserin, Ariel José; Amandi, Analia Adriana; Agents That Learn What Argument to Select In Argumentation-Based Negotiations; International Association for the Development of the Information Society; International Journal on Computer Science and Information Systems; 5; 2; 12-2010; 86-971646-3692CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.iadisportal.org/ijcsis/papers/2010110206.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:47:38Zoai:ri.conicet.gov.ar:11336/243715instacron: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:34982025-09-29 10:47:39.077CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Agents That Learn What Argument to Select In Argumentation-Based Negotiations
title Agents That Learn What Argument to Select In Argumentation-Based Negotiations
spellingShingle Agents That Learn What Argument to Select In Argumentation-Based Negotiations
Monteserin, Ariel José
ARGUMENT SELECTION
ARGUMENTATION-BASED NEGOTIATION
AUTONOMOUS AGENTS
title_short Agents That Learn What Argument to Select In Argumentation-Based Negotiations
title_full Agents That Learn What Argument to Select In Argumentation-Based Negotiations
title_fullStr Agents That Learn What Argument to Select In Argumentation-Based Negotiations
title_full_unstemmed Agents That Learn What Argument to Select In Argumentation-Based Negotiations
title_sort Agents That Learn What Argument to Select In Argumentation-Based Negotiations
dc.creator.none.fl_str_mv Monteserin, Ariel José
Amandi, Analia Adriana
author Monteserin, Ariel José
author_facet Monteserin, Ariel José
Amandi, Analia Adriana
author_role author
author2 Amandi, Analia Adriana
author2_role author
dc.subject.none.fl_str_mv ARGUMENT SELECTION
ARGUMENTATION-BASED NEGOTIATION
AUTONOMOUS AGENTS
topic ARGUMENT SELECTION
ARGUMENTATION-BASED NEGOTIATION
AUTONOMOUS AGENTS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Argument selection is considered the essence of the strategy in argumentation-based negotiation. An agent, which is arguing during a negotiation, has to decide what arguments are the best to persuade the opponent. In fact, in each negotiation step, the agent must select an argument from a set of candidate arguments by applying some selection criterion. For this task, the agent observes some factors of the negotiation context, for instance trust in the opponent, expected utility, among others. Usually, argument selection mechanisms are defined statically. However, as the negotiation context varies from a negotiation to another, defining a static selection mechanism it is not useful. For this reason, we present in this paper a novel approach to personalize argument selection mechanisms in the context of argumentation-based negotiation. The selection mechanism defines a set of preferences that determine how preferable it is to utter an argument in a given context. Our approach maintains a hierarchy of preferences in order to learn new preferences and update the existing ones as the agent experience increases. We tested this approach in a simulated multiagent system and obtained promising results.
Fil: Monteserin, Ariel José. 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: Amandi, Analia Adriana. 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
description Argument selection is considered the essence of the strategy in argumentation-based negotiation. An agent, which is arguing during a negotiation, has to decide what arguments are the best to persuade the opponent. In fact, in each negotiation step, the agent must select an argument from a set of candidate arguments by applying some selection criterion. For this task, the agent observes some factors of the negotiation context, for instance trust in the opponent, expected utility, among others. Usually, argument selection mechanisms are defined statically. However, as the negotiation context varies from a negotiation to another, defining a static selection mechanism it is not useful. For this reason, we present in this paper a novel approach to personalize argument selection mechanisms in the context of argumentation-based negotiation. The selection mechanism defines a set of preferences that determine how preferable it is to utter an argument in a given context. Our approach maintains a hierarchy of preferences in order to learn new preferences and update the existing ones as the agent experience increases. We tested this approach in a simulated multiagent system and obtained promising results.
publishDate 2010
dc.date.none.fl_str_mv 2010-12
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/11336/243715
Monteserin, Ariel José; Amandi, Analia Adriana; Agents That Learn What Argument to Select In Argumentation-Based Negotiations; International Association for the Development of the Information Society; International Journal on Computer Science and Information Systems; 5; 2; 12-2010; 86-97
1646-3692
CONICET Digital
CONICET
url http://hdl.handle.net/11336/243715
identifier_str_mv Monteserin, Ariel José; Amandi, Analia Adriana; Agents That Learn What Argument to Select In Argumentation-Based Negotiations; International Association for the Development of the Information Society; International Journal on Computer Science and Information Systems; 5; 2; 12-2010; 86-97
1646-3692
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.iadisportal.org/ijcsis/papers/2010110206.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv International Association for the Development of the Information Society
publisher.none.fl_str_mv International Association for the Development of the Information Society
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_ 1844614520750735360
score 13.069144

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