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. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning - Concept learning Connectionism and neural nets General Terms Experimentation, Theory Keywords Communication, multi-agent sy...

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Michelle McPartland, Stefano Nolfi, Hussein A. Abbass
• Keywords: Communication, Competitive Multi-agent Systems, GECCO 2005, Multi-agent Systems