Evolution of Collective Behaviors by Minimizing Surprise

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.