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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/6778
Ver los metadatos del registro completo
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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/ |
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openAccess |
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https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
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application/pdf application/pdf application/pdf |
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Elsevier |
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Elsevier |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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