Agents that learn how to generate arguments from other agents

Autores
Amandi, Analia Adriana; Monteserin, Ariel José
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Learning how to argue is a key ability for a negotiator agent. In this paper, we propose an approach that allows agents to learn how to build arguments by observing how other agents argue in a negotiation context. Particularly, our approach enables the agent to infer the rulesfor argument generation that other agents apply to build their arguments. To carry out this goal, the agent stores the arguments uttered by other agents and the facts of the negotiation context where each argument is uttered. Then, an algorithm for fuzzy generalized association rules is applied to discover the desired rules. This kind of algorithm allows us (a) to obtain general rules that can be applied to dierent negotiation contexts; and (b) to deal with the uncertainty about the knowledge of what facts of the context are taken into account by the agents. The experimental results showed that it is possible to infer argument generation rules from a reduced number of observed arguments.
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
Intelligent Agents
Argumentation-Based Negotiation
Fuzzy Generalized Association Rules
Learning
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:11336/6780
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Agents that learn how to generate arguments from other agentsAmandi, Analia AdrianaMonteserin, Ariel JoséIntelligent AgentsArgumentation-Based NegotiationFuzzy Generalized Association RulesLearninghttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Learning how to argue is a key ability for a negotiator agent. In this paper, we propose an approach that allows agents to learn how to build arguments by observing how other agents argue in a negotiation context. Particularly, our approach enables the agent to infer the rulesfor argument generation that other agents apply to build their arguments. To carry out this goal, the agent stores the arguments uttered by other agents and the facts of the negotiation context where each argument is uttered. Then, an algorithm for fuzzy generalized association rules is applied to discover the desired rules. This kind of algorithm allows us (a) to obtain general rules that can be applied to dierent negotiation contexts; and (b) to deal with the uncertainty about the knowledge of what facts of the context are taken into account by the agents. The experimental results showed that it is possible to infer argument generation rules from a reduced number of observed arguments.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; ArgentinaSpringer2014-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/6780Amandi, Analia Adriana; Monteserin, Ariel José; Agents that learn how to generate arguments from other agents; Springer; New Generation Computing; 32; 1; 1-2014; 31-580288-3635enginfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s00354-014-0102-5info:eu-repo/semantics/altIdentifier/doi/10.1007/s00354-014-0102-5info:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:56:51Zoai:ri.conicet.gov.ar:11336/6780instacron: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 09:56:51.436CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Agents that learn how to generate arguments from other agents
title Agents that learn how to generate arguments from other agents
spellingShingle Agents that learn how to generate arguments from other agents
Amandi, Analia Adriana
Intelligent Agents
Argumentation-Based Negotiation
Fuzzy Generalized Association Rules
Learning
title_short Agents that learn how to generate arguments from other agents
title_full Agents that learn how to generate arguments from other agents
title_fullStr Agents that learn how to generate arguments from other agents
title_full_unstemmed Agents that learn how to generate arguments from other agents
title_sort Agents that learn how to generate arguments from other agents
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 Intelligent Agents
Argumentation-Based Negotiation
Fuzzy Generalized Association Rules
Learning
topic Intelligent Agents
Argumentation-Based Negotiation
Fuzzy Generalized Association Rules
Learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Learning how to argue is a key ability for a negotiator agent. In this paper, we propose an approach that allows agents to learn how to build arguments by observing how other agents argue in a negotiation context. Particularly, our approach enables the agent to infer the rulesfor argument generation that other agents apply to build their arguments. To carry out this goal, the agent stores the arguments uttered by other agents and the facts of the negotiation context where each argument is uttered. Then, an algorithm for fuzzy generalized association rules is applied to discover the desired rules. This kind of algorithm allows us (a) to obtain general rules that can be applied to dierent negotiation contexts; and (b) to deal with the uncertainty about the knowledge of what facts of the context are taken into account by the agents. The experimental results showed that it is possible to infer argument generation rules from a reduced number of observed arguments.
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 Learning how to argue is a key ability for a negotiator agent. In this paper, we propose an approach that allows agents to learn how to build arguments by observing how other agents argue in a negotiation context. Particularly, our approach enables the agent to infer the rulesfor argument generation that other agents apply to build their arguments. To carry out this goal, the agent stores the arguments uttered by other agents and the facts of the negotiation context where each argument is uttered. Then, an algorithm for fuzzy generalized association rules is applied to discover the desired rules. This kind of algorithm allows us (a) to obtain general rules that can be applied to dierent negotiation contexts; and (b) to deal with the uncertainty about the knowledge of what facts of the context are taken into account by the agents. The experimental results showed that it is possible to infer argument generation rules from a reduced number of observed arguments.
publishDate 2014
dc.date.none.fl_str_mv 2014-01
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/11336/6780
Amandi, Analia Adriana; Monteserin, Ariel José; Agents that learn how to generate arguments from other agents; Springer; New Generation Computing; 32; 1; 1-2014; 31-58
0288-3635
url http://hdl.handle.net/11336/6780
identifier_str_mv Amandi, Analia Adriana; Monteserin, Ariel José; Agents that learn how to generate arguments from other agents; Springer; New Generation Computing; 32; 1; 1-2014; 31-58
0288-3635
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s00354-014-0102-5
info:eu-repo/semantics/altIdentifier/doi/10.1007/s00354-014-0102-5
info:eu-repo/semantics/altIdentifier/doi/
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
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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 13.069144

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