Journal
ARTIFICIAL LIFE
Volume 9, Issue 3, Pages 255-267Publisher
M I T PRESS
DOI: 10.1162/106454603322392460
Keywords
genetic algorithms; neural networks; aggregation; light pursuit; situated specialization; indexes of collective behavior
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We present a set of experiments in which stimulated robots are evolved for the ability to aggregate and move together toward a light target. By developing and using quantitative indexes that capture the structural properties of the emerged formations, we show that evolved individuals display interesting behavioral patterns in which groups of robots act as a single Unit. Moreover, evolved groups of robots with identical controllers display primitive forms of situated specialization and play different behavioral functions within the group according to the circumstances. Overall, the results presented in the article demonstrate that evolutionary techniques, by exploiting the self-organizing behavioral properties that emerge from the interactions between the robots and between the robots and the environment, are a powerful method for synthesizing collective behavior.
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