Emergent specialization in the extended multi-rover problem

This paper introduces the collective neuro evolution (CONE) method, and compares its efficacy for designing specialization, with a conventional neuro-evolution (NE) method. Specialization was defined at both the individual agent, and at the agent group level. The CONE method was tested comparatively with the conventional NE method in an extension of the multi-rover task domain, where specialization exhibited at both the individual and group level is known to benefit task performance. In the multi-rover domain, the task was for many agents (rovers) to maximize the detection and evaluation of points of interest in a simulated environment, and to communicate gathered information to a base station. The goal of the rover group was to maximize a global evaluation function that measured performance (fitness) of the group. Results indicate that the CONE method was appropriate for facilitating specialization at both the individual and agent group levels, where as, the conventional NE method succeeded only in facilitating individual specialization. As a consequence of emergent specialization derived at both the individual and group levels, rover groups evolved by the CONE method were able to achieve a significantly higher task performance, comparative to groups evolved by the conventional NE method.