The importance of context-dependent learning in negotiation agents

Autores
Kröhling, Dan Ezequiel; Chiotti, Omar Juan Alfredo; Martínez, Ernesto Carlos
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Automated negotiation between artificial agents is essential to deploy Cognitive Computing and Internet of Things. In this sense, the behavior of those negotiation agents depend significantly on the influence of environmental variables, facts, and events, which made up the context of the negotiation game. This context affects not only a given agent preferences and strategies, but also those of his opponents. In spite of this, the existing literature on automated negotiation is scarce about how to properly account for the effect of the context in learning and evolving strategies. In this paper, a novel context-driven representation of the negotiation game is introduced. Also, a simple negotiation agent that queries available information from context variables, internally models them, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes of our context-aware agent against other negotiation agents inthe existing literature, it is shown that it makes no sense to negotiate without taking relevant context variables into account. Our context-aware negotiation agent has been implemented in the GENIUS tool. Results obtained are significant and quite revealing about the role of self-play in learning to negotiate
Fil: Kröhling, Dan Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Chiotti, Omar Juan Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Materia
agent
automated negotition
Reinforcement Learning
Internet of Things,
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/108532
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/108532
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling The importance of context-dependent learning in negotiation agentsKröhling, Dan EzequielChiotti, Omar Juan AlfredoMartínez, Ernesto Carlosagentautomated negotitionReinforcement LearningInternet of Things,https://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Automated negotiation between artificial agents is essential to deploy Cognitive Computing and Internet of Things. In this sense, the behavior of those negotiation agents depend significantly on the influence of environmental variables, facts, and events, which made up the context of the negotiation game. This context affects not only a given agent preferences and strategies, but also those of his opponents. In spite of this, the existing literature on automated negotiation is scarce about how to properly account for the effect of the context in learning and evolving strategies. In this paper, a novel context-driven representation of the negotiation game is introduced. Also, a simple negotiation agent that queries available information from context variables, internally models them, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes of our context-aware agent against other negotiation agents inthe existing literature, it is shown that it makes no sense to negotiate without taking relevant context variables into account. Our context-aware negotiation agent has been implemented in the GENIUS tool. Results obtained are significant and quite revealing about the role of self-play in learning to negotiateFil: Kröhling, Dan Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Chiotti, Omar Juan Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaSociedad Iberoamericana de Inteligencia Artificial2019-05-03info: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/11336/108532Kröhling, Dan Ezequiel; Chiotti, Omar Juan Alfredo; Martínez, Ernesto Carlos; The importance of context-dependent learning in negotiation agents; Sociedad Iberoamericana de Inteligencia Artificial; Inteligencia Artificial; 22; 63; 3-5-2019; 135-1491137-36011988-3064CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://journal.iberamia.org/index.php/intartif/article/view/252info:eu-repo/semantics/altIdentifier/doi/10.4114/intartif.vol22iss63pp135-149info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:13:51Zoai:ri.conicet.gov.ar:11336/108532instacron: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 10:13:52.156CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv The importance of context-dependent learning in negotiation agents
title The importance of context-dependent learning in negotiation agents
spellingShingle The importance of context-dependent learning in negotiation agents
Kröhling, Dan Ezequiel
agent
automated negotition
Reinforcement Learning
Internet of Things,
title_short The importance of context-dependent learning in negotiation agents
title_full The importance of context-dependent learning in negotiation agents
title_fullStr The importance of context-dependent learning in negotiation agents
title_full_unstemmed The importance of context-dependent learning in negotiation agents
title_sort The importance of context-dependent learning in negotiation agents
dc.creator.none.fl_str_mv Kröhling, Dan Ezequiel
Chiotti, Omar Juan Alfredo
Martínez, Ernesto Carlos
author Kröhling, Dan Ezequiel
author_facet Kröhling, Dan Ezequiel
Chiotti, Omar Juan Alfredo
Martínez, Ernesto Carlos
author_role author
author2 Chiotti, Omar Juan Alfredo
Martínez, Ernesto Carlos
author2_role author
author
dc.subject.none.fl_str_mv agent
automated negotition
Reinforcement Learning
Internet of Things,
topic agent
automated negotition
Reinforcement Learning
Internet of Things,
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Automated negotiation between artificial agents is essential to deploy Cognitive Computing and Internet of Things. In this sense, the behavior of those negotiation agents depend significantly on the influence of environmental variables, facts, and events, which made up the context of the negotiation game. This context affects not only a given agent preferences and strategies, but also those of his opponents. In spite of this, the existing literature on automated negotiation is scarce about how to properly account for the effect of the context in learning and evolving strategies. In this paper, a novel context-driven representation of the negotiation game is introduced. Also, a simple negotiation agent that queries available information from context variables, internally models them, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes of our context-aware agent against other negotiation agents inthe existing literature, it is shown that it makes no sense to negotiate without taking relevant context variables into account. Our context-aware negotiation agent has been implemented in the GENIUS tool. Results obtained are significant and quite revealing about the role of self-play in learning to negotiate
Fil: Kröhling, Dan Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Chiotti, Omar Juan Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Fil: Martínez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
description Automated negotiation between artificial agents is essential to deploy Cognitive Computing and Internet of Things. In this sense, the behavior of those negotiation agents depend significantly on the influence of environmental variables, facts, and events, which made up the context of the negotiation game. This context affects not only a given agent preferences and strategies, but also those of his opponents. In spite of this, the existing literature on automated negotiation is scarce about how to properly account for the effect of the context in learning and evolving strategies. In this paper, a novel context-driven representation of the negotiation game is introduced. Also, a simple negotiation agent that queries available information from context variables, internally models them, and learns how to take advantage of this knowledge by playing against himself using reinforcement learning is proposed. Through a set of episodes of our context-aware agent against other negotiation agents inthe existing literature, it is shown that it makes no sense to negotiate without taking relevant context variables into account. Our context-aware negotiation agent has been implemented in the GENIUS tool. Results obtained are significant and quite revealing about the role of self-play in learning to negotiate
publishDate 2019
dc.date.none.fl_str_mv 2019-05-03
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/11336/108532
Kröhling, Dan Ezequiel; Chiotti, Omar Juan Alfredo; Martínez, Ernesto Carlos; The importance of context-dependent learning in negotiation agents; Sociedad Iberoamericana de Inteligencia Artificial; Inteligencia Artificial; 22; 63; 3-5-2019; 135-149
1137-3601
1988-3064
CONICET Digital
CONICET
url http://hdl.handle.net/11336/108532
identifier_str_mv Kröhling, Dan Ezequiel; Chiotti, Omar Juan Alfredo; Martínez, Ernesto Carlos; The importance of context-dependent learning in negotiation agents; Sociedad Iberoamericana de Inteligencia Artificial; Inteligencia Artificial; 22; 63; 3-5-2019; 135-149
1137-3601
1988-3064
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://journal.iberamia.org/index.php/intartif/article/view/252
info:eu-repo/semantics/altIdentifier/doi/10.4114/intartif.vol22iss63pp135-149
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Sociedad Iberoamericana de Inteligencia Artificial
publisher.none.fl_str_mv Sociedad Iberoamericana de Inteligencia Artificial
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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