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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brooks R., “A Robust Layered Control System for a Mobile Robot”, IEEE Journal of Robotics and Automation 2(1), 14–23 (1986).
Chern Han Yong and Risto Miikkulainen, Cooperative Coevolution of Multi-Agent Systems, Technical Report AI01-287
D. Floreano, S. Nolfi, and F. Mondada, Competitive Co-Evolutionary Robotics: From Theory to Practice. In R. Pfeifer, editor, From Animals to Animats V: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior, MIT Press-Bradford Books, Cambridge, MA, 1998.
Hitoshi Iba, Emergent cooperation for Multiple Agents using Genetic Programming, in Parallel Problem Solving form Nature IV (PPSN96), 1996, 32–41
Hitoshi Iba, Evolving Multiple Agents by Genetic Programming, Genetic Programming 3, Spector, L. Langdon, W.B., O’Reilly, U.-A., and Angeline, P.A.,(eds), MIT Press, pp. 447–466, 1999.
M. Terao and Hitoshi Iba Controlling Effective Introns for Multi-Agent Learning by Genetic Programming, in Proc. of the Genetic and Evolutionary Computation Conference (GECCO2000), pp. 419–426, 2000
J.R. Koza, Evolution of subsumption using genetic programming, in F.J. Varela and P. Bourgine (Eds.) Toward a Practice of Autonomous Systems, pp. 110–119 Cambridge, MA: MIT Press. 1992.
K. Yanai and H. Iba Multi-agent Robot Learning by Means of Genetic Programming: Solving an Escape problem, in Evolvable Systems: From Biology to Hardware, 4th International Conference (ICES2001), pp. 192–203. 2001
Matt Quinn, A comparison of approaches to the evolution of homogeneous multi-robot teams, In Proceedings of the Congress on Evolutionary Computation (GECCO2001) pp. 128–135, Seoul, S. Korea. IEEE Press, 2001
Nolfi S. and Floreano D., Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. Cambridge, MA: MIT Press/Bradford Books, 2000.
P. Nordin and W. Banzhaf., An on-line method to evolve behavior and to control a miniature robot in real time with genetic programming, Adaptive Behavior, 5(2):107–140, 1997
W.F. Punch and W.M. Rand, GP+Echo+Subsumption — Improved Problem Solving, in Proc. of the Genetic and Evolutionary Computation Conference (GECCO2000), pp. 411–418, 2000
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, H., Iba, H. (2003). Multi-agent Learning of Heterogeneous Robots by Evolutionary Subsumption. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_64
Download citation
DOI: https://doi.org/10.1007/3-540-45110-2_64
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40603-7
Online ISBN: 978-3-540-45110-5
eBook Packages: Springer Book Archive