Learning useful communication structures for groups of agents

Autores
Goebels, Andreas
Año de publicación
2006
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Coordination of altruistic agents to solve optimization problems can be significantly enhanced when inter-agent communication is allowed. In this paper we present an evolutionary approach to learn optimal communication structures for groups of agents. The agents learn to solve the Online Partitioning Problem, but our ideas can easily be adapted to other problem fields. With our approach we can find the optimal communication partners for each agent in a static environment. In a dynamic environment we figure out a simple relation between each position of agents in space and the optimal number of communication partners. A concept for the establishment of relevant communication connections between certain agents will be shown whereby the space the agents are located in will be divided into several regions. These regions will be described mathematically. After a learning process the algorithm assigns an appropriate number of communication partners for every agent in an - arbitrary located - group.
1st IFIP International Conference on Biologically Inspired Cooperative Computing - Communication
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Multiagent systems
Learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/24007
Acceder
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network_name_str SEDICI (UNLP)
spelling Learning useful communication structures for groups of agentsGoebels, AndreasCiencias InformáticasMultiagent systemsLearningCoordination of altruistic agents to solve optimization problems can be significantly enhanced when inter-agent communication is allowed. In this paper we present an evolutionary approach to learn optimal communication structures for groups of agents. The agents learn to solve the Online Partitioning Problem, but our ideas can easily be adapted to other problem fields. With our approach we can find the optimal communication partners for each agent in a static environment. In a dynamic environment we figure out a simple relation between each position of agents in space and the optimal number of communication partners. A concept for the establishment of relevant communication connections between certain agents will be shown whereby the space the agents are located in will be divided into several regions. These regions will be described mathematically. After a learning process the algorithm assigns an appropriate number of communication partners for every agent in an - arbitrary located - group.1st IFIP International Conference on Biologically Inspired Cooperative Computing - CommunicationRed de Universidades con Carreras en Informática (RedUNCI)2006-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/24007enginfo:eu-repo/semantics/altIdentifier/isbn/0-387-34632-5info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:41Zoai:sedici.unlp.edu.ar:10915/24007Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:41.374SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Learning useful communication structures for groups of agents
title Learning useful communication structures for groups of agents
spellingShingle Learning useful communication structures for groups of agents
Goebels, Andreas
Ciencias Informáticas
Multiagent systems
Learning
title_short Learning useful communication structures for groups of agents
title_full Learning useful communication structures for groups of agents
title_fullStr Learning useful communication structures for groups of agents
title_full_unstemmed Learning useful communication structures for groups of agents
title_sort Learning useful communication structures for groups of agents
dc.creator.none.fl_str_mv Goebels, Andreas
author Goebels, Andreas
author_facet Goebels, Andreas
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Multiagent systems
Learning
topic Ciencias Informáticas
Multiagent systems
Learning
dc.description.none.fl_txt_mv Coordination of altruistic agents to solve optimization problems can be significantly enhanced when inter-agent communication is allowed. In this paper we present an evolutionary approach to learn optimal communication structures for groups of agents. The agents learn to solve the Online Partitioning Problem, but our ideas can easily be adapted to other problem fields. With our approach we can find the optimal communication partners for each agent in a static environment. In a dynamic environment we figure out a simple relation between each position of agents in space and the optimal number of communication partners. A concept for the establishment of relevant communication connections between certain agents will be shown whereby the space the agents are located in will be divided into several regions. These regions will be described mathematically. After a learning process the algorithm assigns an appropriate number of communication partners for every agent in an - arbitrary located - group.
1st IFIP International Conference on Biologically Inspired Cooperative Computing - Communication
Red de Universidades con Carreras en Informática (RedUNCI)
description Coordination of altruistic agents to solve optimization problems can be significantly enhanced when inter-agent communication is allowed. In this paper we present an evolutionary approach to learn optimal communication structures for groups of agents. The agents learn to solve the Online Partitioning Problem, but our ideas can easily be adapted to other problem fields. With our approach we can find the optimal communication partners for each agent in a static environment. In a dynamic environment we figure out a simple relation between each position of agents in space and the optimal number of communication partners. A concept for the establishment of relevant communication connections between certain agents will be shown whereby the space the agents are located in will be divided into several regions. These regions will be described mathematically. After a learning process the algorithm assigns an appropriate number of communication partners for every agent in an - arbitrary located - group.
publishDate 2006
dc.date.none.fl_str_mv 2006-08
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
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dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/0-387-34632-5
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
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reponame_str SEDICI (UNLP)
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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