creators_name: Nehmzow, Ulrich
editors_name: Prince, Christopher G.
editors_name: Demiris, Yiannis
editors_name: Marom, Yuval
editors_name: Kozima, Hideki
editors_name: Balkenius, Christian
type: confpaper
datestamp: 2003-10-04
lastmod: 2011-03-11 08:55:04
metadata_visibility: show
title: Physically Embedded Genetic Algorithm Learning in Multi-Robot Scenarios: The PEGA algorithm
ispublished: pub
subjects: comp-sci-mach-learn
subjects: comp-sci-art-intel
subjects: comp-sci-robot
full_text_status: public
keywords: mobile robots, collaborative learning, genetic algorithm, PEGA
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.
date: 2002
date_type: published
volume: 94
publisher: Lund University Cognitive Studies
pagerange: 115-123
refereed: TRUE
citation:   Nehmzow, Ulrich  (2002) Physically Embedded Genetic Algorithm Learning in Multi-Robot Scenarios: The PEGA algorithm.  [Conference Paper]     
document_url: http://cogprints.org/2521/1/Nehmzow.pdf