A Neuroevolutionary Approach to Adaptive Multi-agent Teams
Bobby D. Bryant, Risto P Miikkulainen · Studies in systems, decision and control · 2018
A multi-agent architecture called the Adaptive Team of Agents (ATA) is introduced, wherein homogeneous agents adopt specific roles in a team dynamically in order to address all the sub-tasks necessary to meet the team’s goals. Artificial neural networks are then trained by neuroevolutionNeuroevolution to produce an example of such a team, trained to solve the problem posed by a simple strategy game. The evolutionary algorithm is found to induce the necessary in situ adaptivity of behavior into the agents, even when controlled by stateless feed-forward networks.