Artículo

Multiagent team formation performed by operant learning: an animat approach

Gutnisky, D. A.; Zelmann, R.; Zanutto, Bonifacio SilvanoIcon
Fecha de publicación: 12/2006
Editorial: Institute Of Electrical And Electronics Engineers
Revista: Proceedings of International Joint Conference on Neural Networks
ISSN: 2161-4393
e-ISSN: 2161-4407
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:

Resumen

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.
Palabras clave: Operant Behavior , Multiagent System , Neural Networks , Reinforcement Learning
 
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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/18532
DOI: http://dx.doi.org/ 10.1109/IJCNN.2006.247228
URL: http://ieeexplore.ieee.org/document/1716498/
Colecciones
Articulos(IBYME)
Articulos de INST.DE BIOLOGIA Y MEDICINA EXPERIMENTAL (I)
Citación
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
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