Task-Driven Species in Evolutionary Robotic Teams
This paper deals with the problem of obtaining coordinated behavior in multirobot systems by evolution. More specifically, we are interested in using a method that allows the emergence of different species if they are required by the task, that is, if specialization provides an advantage in the completion of the task, without the designer having to predefine the best way to solve it. To this end, in this work we have applied a co-evolutionary algorithm called ASiCo (Asynchronous Situated Co-evolution) which is based on an open-ended evolution of the robots in their environment. In this environment the robots are born, mate and die throughout the generations as in an artificial life system. In order to show that ASiCo is capable of obtaining species automatically if they are advantageous, here we apply it to a collective gathering and construction task where homogeneous teams are suboptimal.
KeywordsMulti-robot Systems Evolutionary Algorithms Coordination Collective Intelligence
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