Multi-agent Learning of Heterogeneous Robots by Evolutionary Subsumption

Abstract. Many multi-robot systems are heterogeneous cooperative systems, systems consisting of different species of robots cooperating with each other to achieve a common goal. This paper presents the emergence of cooperative behaviors of heterogeneous robots by means of GP. Since directly using GP to generate a controller for complex behaviors is inefficient and intractable, especially in the domain of multi-robot systems, we propose an approach called Evolutionary Subsumption, which applies GP to subsumption architecture. We test our approach in an “eye”-“hand” cooperation problem. By comparing our approach with direct GP and artificial neural network (ANN) approaches, our experimental results show that ours is more efficient in emergence of complex behaviors.

• Type: Conference
• Year: 2003
• Where: GECCO
• Authors: Hongwei Liu, Hitoshi Iba
• Keywords: Complex Behaviors, GECCO 2003, Many Multi-robot Systems, Multi-robot Systems