Trading off impact and mutation of knowledge by cooperatively learning robots

We present a socially inspired approach that allows agents in Multi-Agent Systems to speed up their own learning process through communication. Thereby, they are able to trade off impact of knowledge by mutation dependent on the recent performance of the interacting agents. This is inspired by socia...

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Detalles Bibliográficos
Autores principales: Richert, Willi, Kleinjohann, Bernd, Kleinjohann, Lisa
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2006
Materias:
Ciencias Informáticas
Robotics
Collaborative learning
Multiagent systems
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/24019
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata

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