Multi-agent learning of heterogeneous robots by evolutionary subsumption

  • Authors:

  • Hongwei Liu;Hitoshi Iba

  • Affiliations:

  • Graduate School of Frontier Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan and School of Computer and Information, Hefei University of Technology, Hefei, China;Graduate School of Frontier Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan

  • Venue:
  • GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
  • Year:
  • 2003

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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.