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Physically Embedded Genetic Algorithm Learning in Multi-Robot Scenarios: The PEGA algorithm

Nehmzow, Ulrich (2002) Physically Embedded Genetic Algorithm Learning in Multi-Robot Scenarios: The PEGA algorithm. [Conference Paper]

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Abstract

We present experiments in which a group of autonomous mobile robots learn to perform fundamental sensor-motor tasks through a collaborative learning process. Behavioural strategies, i.e. motor responses to sensory stimuli, are encoded by means of genetic strings stored on the individual robots, and adapted through a genetic algorithm (Mitchell, 1998) executed by the entire robot collective: robots communicate their own strings and corresponding fitness to each other, and then execute a genetic algorithm to improve their individual behavioural strategy. The robots acquired three different sensormotor competences, as well as the ability to select one of two, or one of three behaviours depending on context ("behaviour management"). Results show that fitness indeed increases with increasing learning time, and the analysis of the acquired behavioural strategies demonstrates that they are effective in accomplishing the desired task.

Item Type:Conference Paper
Keywords:mobile robots, collaborative learning, genetic algorithm, PEGA
Subjects:Computer Science > Machine Learning
Computer Science > Artificial Intelligence
Computer Science > Robotics
ID Code:2521
Deposited By:Prince, Dr Christopher G.
Deposited On:04 Oct 2003
Last Modified:11 Mar 2011 08:55

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