Using reinforcement learning and artificial evolution for the detection of group identities in complex adaptive artificial societies

We present a computational framework capable of inferring the existence of groups, built upon social networks of reciprocal friendship, in Complex Adaptive Artificial Societies (CAAS). Our modelling framework infers the group identities by following two steps: first, it aims to learn the ongoing levels of cooperation among the agents and, second, it applies evolutionary computation, based on the learned cooperation values, to partition the agents into groups. Experimental investigations, based on CAAS of agents who interact with each other by means of the Ultimatum Game, show that a cooperation learning phase, based on Reinforcement Learning, can provide highly promising results for minimising the mismatch between the existing and the inferred groups, for two different society sizes under investigation.