Evolution of signalling in a group of robots controlled by dynamic neural networks (original) (raw)
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Artificial Life, 2013
Organisms that live in groups, from microbial symbionts to social insects and schooling fish, exhibit a number of highly efficient cooperative behaviours, often based on role taking and specialisation. These behaviours are relevant not only for the biologist but also for the engineer interested in decentralized collective robotics. We address these phenomena by carrying out experiments with groups of two simulated robots controlled by neural networks whose connection weights are evolved by using genetic algorithms. These algorithms and controllers are well suited to autonomously find solutions to decentralized collective robotic tasks based on principles of self-organization. The paper first presents a taxonomy of role taking and specialisation mechanisms related to evolved neural-network controllers. Then it introduces two cooperation tasks which can be accomplished by either role taking or specialisation and uses these tasks to compare four different genetic algorithms to evaluate their capacity to evolve a suitable behavioural strategy which depends on the task demands. Interestingly, only one of the four algorithms, which appears to have more biological plausibility, is capable of evolving role taking or specialisation when they are needed. The results are relevant for both collective robotics and biology as they can provide useful hints on the different processes that can lead to the emergence of specialisation in robots and organisms.
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