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PDF 3.9 MB
DOI: http://dx.doi.org/10.7551/978-0-262-32621-6-ch049
Pages 302-309
First published 30 July 2014

Asynchronous Evolution: Emergence of Signal-Based Swarming

Olaf Witkowski and Takashi Ikegami

Abstract (Excerpt)

Since Reynolds boids, swarming behavior has often been reproduced in artificial models, but the conditions leading to its emergence are still subject to research, with candidates ranging from obstacle avoidance to virtual leaders. In this paper, we present a multi-agent model in which individuals develop swarming using only their ability to listen to each others signals. Our model uses an original asynchronous genetic algorithm to evolve a population of agents controlled by artificial neural networks, looking for an invisible resource in a 3D environment. The results demonstrate that agents use the information exchanged between them via signaling to form temporary leader-follower relations allowing them to flock together.