This research is positioned in the context of controller design for (simulated) multi-robot applications. Inspired by research in survey and exploration of unknown environments where a multi-robot system is to discover features of interest given strict time and energy constraints, we defined an abstract task domain with adaptable features of interest. Additionally, we parameterized the behavioral features of the robots, so that we could classify behavioral specialization in the space of these parameters. This allowed systematic experimentation over a range of task instances and types of specialization in order to investigate the advantage of specialization. These experiments also delivered a novel neuro-evolution approach to controller design, called the collective specialization method. Results elucidated that this method derived multi-robot system controllers that outperformed a high performance heuristic and conventional neuro-evolution method.
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