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...
Guardado en:
| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2006
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/24019 |
| Aporte de: |
| Sumario: | 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 social interaction of humans, where the opinions of experts have greater impact on the overall opinion and are incorporated more exactly than those of newbies. The approach is successfully evaluated in a simulation in which mobile robots have to accomplish a task while taking care of timely recharging their resources |
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