A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation

Autores
Amandi, Analia Adriana; Monteserin, Ariel José
Año de publicación
2013
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, must 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 policy. Following this policy, the agent observes some factors of the negotiation context, for instance: trust in the opponent and expected utility of the negotiated agreement, among others. Usually, argument selection policies are dened statically. However, as the negotiation context varies from a negotiation to another, dening a static selection policy it is not useful. Therefore, the agent should modify its selection policy in order to adapt it to the dierent negotiation contexts as the agent´s experience increases. In this paper, we present a reinforcement learning approach that allows the agent to improve the argument selection eciency by updating the argument selection policy. To carry out this goal, the argument selection mechanism is represented as a reinforcement learning model. We tested this approach in a multiagent system, in a stationary as well as in a dynamic environment, and obtained promising results in both.
Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Materia
REINFORCEMENT LEARNING
ARGUMENT SELECTION
ARGUMENTATION-BASED NEGOTIATION
AUTONOMOUS AGENTS
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:11336/6778

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network_name_str CONICET Digital (CONICET)
spelling A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based NegotiationAmandi, Analia AdrianaMonteserin, Ariel JoséREINFORCEMENT LEARNINGARGUMENT 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, must 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 policy. Following this policy, the agent observes some factors of the negotiation context, for instance: trust in the opponent and expected utility of the negotiated agreement, among others. Usually, argument selection policies are dened statically. However, as the negotiation context varies from a negotiation to another, dening a static selection policy it is not useful. Therefore, the agent should modify its selection policy in order to adapt it to the dierent negotiation contexts as the agent´s experience increases. In this paper, we present a reinforcement learning approach that allows the agent to improve the argument selection eciency by updating the argument selection policy. To carry out this goal, the argument selection mechanism is represented as a reinforcement learning model. We tested this approach in a multiagent system, in a stationary as well as in a dynamic environment, and obtained promising results in both.Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaElsevier2013-05info: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/11336/6778Amandi, Analia Adriana; Monteserin, Ariel José; A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation; Elsevier; Expert Systems with Applications; 40; 6; 5-2013; 2182-21880957-4174enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0957417412011694info:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2012.10.045info:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:29:58Zoai:ri.conicet.gov.ar:11336/6778instacron: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:34982025-10-15 14:29:59.012CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation
title A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation
spellingShingle A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation
Amandi, Analia Adriana
REINFORCEMENT LEARNING
ARGUMENT SELECTION
ARGUMENTATION-BASED NEGOTIATION
AUTONOMOUS AGENTS
title_short A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation
title_full A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation
title_fullStr A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation
title_full_unstemmed A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation
title_sort A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation
dc.creator.none.fl_str_mv Amandi, Analia Adriana
Monteserin, Ariel José
author Amandi, Analia Adriana
author_facet Amandi, Analia Adriana
Monteserin, Ariel José
author_role author
author2 Monteserin, Ariel José
author2_role author
dc.subject.none.fl_str_mv REINFORCEMENT LEARNING
ARGUMENT SELECTION
ARGUMENTATION-BASED NEGOTIATION
AUTONOMOUS AGENTS
topic REINFORCEMENT LEARNING
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, must 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 policy. Following this policy, the agent observes some factors of the negotiation context, for instance: trust in the opponent and expected utility of the negotiated agreement, among others. Usually, argument selection policies are dened statically. However, as the negotiation context varies from a negotiation to another, dening a static selection policy it is not useful. Therefore, the agent should modify its selection policy in order to adapt it to the dierent negotiation contexts as the agent´s experience increases. In this paper, we present a reinforcement learning approach that allows the agent to improve the argument selection eciency by updating the argument selection policy. To carry out this goal, the argument selection mechanism is represented as a reinforcement learning model. We tested this approach in a multiagent system, in a stationary as well as in a dynamic environment, and obtained promising results in both.
Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Monteserin, Ariel José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria 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, must 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 policy. Following this policy, the agent observes some factors of the negotiation context, for instance: trust in the opponent and expected utility of the negotiated agreement, among others. Usually, argument selection policies are dened statically. However, as the negotiation context varies from a negotiation to another, dening a static selection policy it is not useful. Therefore, the agent should modify its selection policy in order to adapt it to the dierent negotiation contexts as the agent´s experience increases. In this paper, we present a reinforcement learning approach that allows the agent to improve the argument selection eciency by updating the argument selection policy. To carry out this goal, the argument selection mechanism is represented as a reinforcement learning model. We tested this approach in a multiagent system, in a stationary as well as in a dynamic environment, and obtained promising results in both.
publishDate 2013
dc.date.none.fl_str_mv 2013-05
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/11336/6778
Amandi, Analia Adriana; Monteserin, Ariel José; A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation; Elsevier; Expert Systems with Applications; 40; 6; 5-2013; 2182-2188
0957-4174
url http://hdl.handle.net/11336/6778
identifier_str_mv Amandi, Analia Adriana; Monteserin, Ariel José; A Reinforcement Learning Approach to Improve the Argument Selection Effectiveness in Argumentation-based Negotiation; Elsevier; Expert Systems with Applications; 40; 6; 5-2013; 2182-2188
0957-4174
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0957417412011694
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2012.10.045
info:eu-repo/semantics/altIdentifier/doi/
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
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
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score 12.891075