Multiagent team formation performed by operant learning: an animat approach

Autores
Gutnisky, D. A.; Zelmann, R.; Zanutto, Bonifacio Silvano
Año de publicación
2006
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
An animat approach to dynamic team formation in a group of distributed robots is studied. The goal is that robots learn to align with the others in order to form a row or a column without having communication among them, just local sensing and a reinforcement signal. The action of the robot is controlled by a biologically plausible neural network model of operant learning. The remarkable performance achieved by the proposed model allows the building of new artificial intelligence agents based on neurobiology, psychology and ethology research.
Fil: Gutnisky, D. A..
Fil: Zelmann, R..
Fil: Zanutto, Bonifacio Silvano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina
Materia
Operant Behavior
Multiagent System
Neural Networks
Reinforcement 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:11336/18532
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/18532
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Multiagent team formation performed by operant learning: an animat approachGutnisky, D. A.Zelmann, R.Zanutto, Bonifacio SilvanoOperant BehaviorMultiagent SystemNeural NetworksReinforcement Learninghttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3https://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2An animat approach to dynamic team formation in a group of distributed robots is studied. The goal is that robots learn to align with the others in order to form a row or a column without having communication among them, just local sensing and a reinforcement signal. The action of the robot is controlled by a biologically plausible neural network model of operant learning. The remarkable performance achieved by the proposed model allows the building of new artificial intelligence agents based on neurobiology, psychology and ethology research.Fil: Gutnisky, D. A..Fil: Zelmann, R..Fil: Zanutto, Bonifacio Silvano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaInstitute Of Electrical And Electronics Engineers2006-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/18532Gutnisky, D. A.; Zelmann, R.; Zanutto, Bonifacio Silvano; Multiagent team formation performed by operant learning: an animat approach; Institute Of Electrical And Electronics Engineers; Proceedings of International Joint Conference on Neural Networks; 2006; 12-2006; 2944-29502161-43932161-4407CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/ 10.1109/IJCNN.2006.247228info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/1716498/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 Técnicas2025-10-15T14:21:25Zoai:ri.conicet.gov.ar:11336/18532instacron: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 14:21:25.674CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Multiagent team formation performed by operant learning: an animat approach
title Multiagent team formation performed by operant learning: an animat approach
spellingShingle Multiagent team formation performed by operant learning: an animat approach
Gutnisky, D. A.
Operant Behavior
Multiagent System
Neural Networks
Reinforcement Learning
title_short Multiagent team formation performed by operant learning: an animat approach
title_full Multiagent team formation performed by operant learning: an animat approach
title_fullStr Multiagent team formation performed by operant learning: an animat approach
title_full_unstemmed Multiagent team formation performed by operant learning: an animat approach
title_sort Multiagent team formation performed by operant learning: an animat approach
dc.creator.none.fl_str_mv Gutnisky, D. A.
Zelmann, R.
Zanutto, Bonifacio Silvano
author Gutnisky, D. A.
author_facet Gutnisky, D. A.
Zelmann, R.
Zanutto, Bonifacio Silvano
author_role author
author2 Zelmann, R.
Zanutto, Bonifacio Silvano
author2_role author
author
dc.subject.none.fl_str_mv Operant Behavior
Multiagent System
Neural Networks
Reinforcement Learning
topic Operant Behavior
Multiagent System
Neural Networks
Reinforcement Learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv An animat approach to dynamic team formation in a group of distributed robots is studied. The goal is that robots learn to align with the others in order to form a row or a column without having communication among them, just local sensing and a reinforcement signal. The action of the robot is controlled by a biologically plausible neural network model of operant learning. The remarkable performance achieved by the proposed model allows the building of new artificial intelligence agents based on neurobiology, psychology and ethology research.
Fil: Gutnisky, D. A..
Fil: Zelmann, R..
Fil: Zanutto, Bonifacio Silvano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina
description An animat approach to dynamic team formation in a group of distributed robots is studied. The goal is that robots learn to align with the others in order to form a row or a column without having communication among them, just local sensing and a reinforcement signal. The action of the robot is controlled by a biologically plausible neural network model of operant learning. The remarkable performance achieved by the proposed model allows the building of new artificial intelligence agents based on neurobiology, psychology and ethology research.
publishDate 2006
dc.date.none.fl_str_mv 2006-12
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/11336/18532
Gutnisky, D. A.; Zelmann, R.; Zanutto, Bonifacio Silvano; Multiagent team formation performed by operant learning: an animat approach; Institute Of Electrical And Electronics Engineers; Proceedings of International Joint Conference on Neural Networks; 2006; 12-2006; 2944-2950
2161-4393
2161-4407
CONICET Digital
CONICET
url http://hdl.handle.net/11336/18532
identifier_str_mv Gutnisky, D. A.; Zelmann, R.; Zanutto, Bonifacio Silvano; Multiagent team formation performed by operant learning: an animat approach; Institute Of Electrical And Electronics Engineers; Proceedings of International Joint Conference on Neural Networks; 2006; 12-2006; 2944-2950
2161-4393
2161-4407
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/ 10.1109/IJCNN.2006.247228
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/1716498/
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
dc.publisher.none.fl_str_mv Institute Of Electrical And Electronics Engineers
publisher.none.fl_str_mv Institute Of Electrical And Electronics Engineers
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_ 1846082602253942784
score 13.124843

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