A neuroevolutionary approach to adaptive multi-agent teams

Abstract

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 neuroevolution 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.