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PDF 1.7 MB
DOI: http://dx.doi.org/10.7551/978-0-262-32621-6-ch055
Pages 344-351
First published 30 July 2014

Evolution of Collective Behaviors by Minimizing Surprise

Heiko Hamann

Abstract (Excerpt)

Similarly to evolving controllers for single robots also controllers for groups of robots can be generated by applying evolutionary algorithms. Usually a fitness function rewards desired behavioral features. Here we investigate an alternative method that generates collective behaviors almost only as a by-product. We roughly follow the idea of Helmholtz that perception is a process based on probabilistic inference and evolve an internal model that is supposed to predict the agent's future perceptions. Separated from this prediction model the agent also evolves a regular controller. Direct selective pressure, however, is only effective on the prediction model by minimizing prediction error (surprise). Our results show that a number of basic collective behaviors emerge by this approach, such as dispersion, aggregation, and flocking. The probability that a certain behavior emerges and also the difficulty of making correct predictions depends on the swarm density. The reported method has potential to be another simple approach to open-ended evolution analogical to the search for novelty.