Trading off impact and mutation of knowledge by cooperatively learning robots

Richert, Willi et al · SEDICI UNLP · 2006

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Richert, Willi et al

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SEDICI UNLP

Year

2006

Language

English

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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 1st IFIP International Conference on Biologically Inspired Cooperative Computing - Robotics and Sensor Networks Red de Universidades con Carreras en Informática (RedUNCI)

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APA 7

Richert, W. E. A. (2006). Trading off impact and mutation of knowledge by cooperatively learning robots. SEDICI UNLP. http://sedici.unlp.edu.ar/handle/10915/24019

MLA

Richert, Willi et al. Trading off impact and mutation of knowledge by cooperatively learning robots. SEDICI UNLP, 2006. http://sedici.unlp.edu.ar/handle/10915/24019.

Chicago

Richert, Willi et al. 2006. Trading off impact and mutation of knowledge by cooperatively learning robots. SEDICI UNLP. http://sedici.unlp.edu.ar/handle/10915/24019.

Harvard

Richert, W. E. A. 2006, Trading off impact and mutation of knowledge by cooperatively learning robots, SEDICI UNLP, available at: http://sedici.unlp.edu.ar/handle/10915/24019 [Accessed 7 Aug. 2026].

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Title: Trading off impact and mutation of knowledge by cooperatively learning robots

Author / contributors: Richert, Willi et al

Publisher: SEDICI UNLP

Publication year: 2006

Language: English

Subjects

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Ciencias Informáticas; Robotics; Collaborative learning; Multiagent systems