8.5 Summary
In this chapter, two possible approaches for evolving complex behaviors were discussed. In the first approach, the GP is used to explore possible hierarchy in the solution through implementing ADF and maintaining a subroutine library or using neural networks as primitive functions.
In the second approach, human programmer set the architecture of the robot controller and then the GP is used to evolve each module of this architecture. Two examples of architectures were discussed, the subsumption architecture and action selection architecture. Two experiments were presented to demonstrate this approach. The first used subsumption architecture to control a team of two robots with different capabilities to implement a cooperative behavior. The second experiment used action selection architecture to allow switching between the simpler behaviors that constitute the main behavior.
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
K-Team (1999) Khepera User Manual,” Lasuanne, Switzerland
Mondada F, Franz F, Paolo I (1993) Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithm. Proceedings of the Third International Symposium on Experimental Robotics, Kyoto, Japan
Koza J (1992) Hierarchical Automatic Function Definition in Genetic Programming. Proceedings of Workshop on the Foundations of Genetic Algorithms and Classifier Systems 297–318, Morgan Kaufmann Publishers Inc., Vail Colorado, USA
Koza J (1993) Simultaneous Discovery of Detectors and a Way of Using the Detectors via Genetic Programming. IEEE International Conference on Neural Networks 3:1794–1801, San Francisco, NJ, USA
Koza J (1993) Simultaneous Discovery of Reusable Detectors and Subroutines Using Genetic Programming. Proceedings of the 5th International Conference on Genetic Algorithms ICGA:295–302, Morgan Kaufmann Publishers Inc.
Hondo N, Iba H, Kakazu Y (1996) Sharing and Refinement for Reusable Subroutines of Genetic Programming. Proceedings of IEEE International Conference on Evolutionary Computation ICEC: 565–570
Yanai K, Iba H (2001) Multi-agent Robot Learning by Means of Genetic Programming: Solving an Escape Problem. Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware: 192–203, Springer-Verlag
Iba H, Terao M (2000) Controlling effecective Introns for Multi-Agent Learning by Genetic Programming. Proceedings of Genetic and Evolutionary Conference GECCO: 419–426
Floreano D, Mondada F (1996) Evolution of Homing Navigation in a Real Mobile Robot. IEEE Transactions on Systems, Man, and Cybernetics (B) 2:396–407
Hulse M, Lara B, Pasemann F, Steinmetz U (2001) Evolving Neural Behaviour Control for Autonomous Robots. Max-Planck Institute for Mathematics in the Sciences, Leipzig, Germany
Haykin S (1999), Neural Networks: a Comprehensive Foundation, Second edition, Prentice Hall, Upper Saddle River, NJ
Lee K, Byoung-Tak Z (2000) Learning Robot Behaviors by Evolving Genetic Programs. 26th Annual Confjerence of the IEEE Industrial Electronics Society, IECON 2000 4:2867–2872
Brooks R (1986) A Robust Layered Control System For Mobile Robots. IEEE Robotics and Automation 2:14–23
Arkin R (1998) Behavior-Based Robotics. The MIT press, Cambridge, USA
Koza J (1993) Evolution of Subsumption Using Genetic Programming. In: F. Varela and P. Bourgine (eds) Toward a Practice of Autonomous Systems 110–119, Cambridge, MIT Press, USA
Kamio S, Hongwei L, Mitsuhasi H, Iba H (2003) Researches on Ingeniously Behaving Agents. Proceedings of NASA/DoD Conference on Evolvable Hardware 208–217
Maes P (1989) The Dynamics of Action Selection. Proceedings of the Eleventh International Joint Conference on Articial Intelligence IJCAI89 2:991–998
Lee W, Hallam J, Lund H (1997) Learning Complex Robot Behaviours by Evolutionary Approaches. 6th European Workshop on Learning Robots EWLR6 42–51
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Botros, M. (2006). Evolving Complex Robotic Behaviors Using Genetic Programming. In: Nedjah, N., Mourelle, L.d.M., Abraham, A. (eds) Genetic Systems Programming. Studies in Computational Intelligence, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32498-4_8
Download citation
DOI: https://doi.org/10.1007/3-540-32498-4_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29849-6
Online ISBN: 978-3-540-32498-0
eBook Packages: EngineeringEngineering (R0)