Emergence of communication in competitive multi-agent systems: A Pareto multi-objective approach

In this paper we investigate the emergence of communication in competitive multi-agent systems. A competitive environment is created with two teams of agents competing in an exploration task; the quickest team to explore the largest area wins. One team uses indirect communication and is controlled by an artificial neural network evolved using a Pareto multi-objective approach. The second team uses direct communication and a fixed strategy for exploration. A comparison is made between agents with and without communication. Results show that as the fitness function vary differing exploration strategies emerge. Experiments with communication produced cooperative strategies; while the experiments without communication produced effective strategies but with individuals acting independently.