Abstract
Co-evolution has recently been receiving increased attention as a method for multi agent simultaneous learning. This paper discusses how multiple robots can emerge cooperative behaviors through co-evolutionary processes. As an example task, a simplified soccer game with three learning robots is selected and a, GP (genetic programming) method is applied to individual population corresponding to each robot so as to obtain cooperative and competitive behaviors through evolutionary processes. The complexity of the problem can be explained twofold: co-evolution for cooperative behaviors needs exact synchronization of mutual evolutions, and three robot co-evolution requires well-complicated environment setups that may gradually change from simpler to more complicated situations so that they can obtain cooperative and competitive behaviors simultaneously in a wide range of search area in various kinds of aspects. Simulation results are shown, and a discussion is given.
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M. Asada, S. Noda, S. Tawaratumida, and K. Hosoda. Purposive Behavior Acquisition for a Real Robot by Vision-Based Reinforcement Learning. Machine Learning, 23:279–303, 1996.
D. Cliff and G. F. Miller. Co-evolution of Pursuit and Evasion II: Simulation Methods and Results. In Proc. of the 4th International Conference on Simulation of Adaptive Behavior: From Animals to Animats 4., pages 506–515. 1996.
D. Floreano and S. Nolfi. Adaptive Behavior in Competeing Co-Evolving Species. In Fourth European Conference on Artificial Life (ECAL97), pages 378–387, 1997.
H. Kitano, M. Asada, Y. Kuniyoshi, I. Noda, E. Osawa, and H. Matsubara. RoboCup A Challenge Problem for AI. AI Magazine, 18(1):73–85, 1997.
J. R. Koza. Genetic Programming I: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992.
J. R. Koza. Genetic Programming II: Automatic Discovery of Reusable SubPrograms. MIT Press, 1991.
S. Luke, C. Hohn, J. Farris, G. Jackson, and J. Hendler. Co-Evolving Soccer Softbot Team Coordination with Genetic Programming. In Proc. of the RoboCup-97 Workshop at the 15th International Joint Conference on Artificial Intelligence (IJCAI97), pages 115–118, 1997.
E. Uchibe, M. Asada, and K. Hosoda. Behavior Coordination for a Mobile Robot Using Modular Reinforcement Learning. In Proc. of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1329–1336, 1996.
E. Uchibe, M. Asada, and K. Hosoda. Cooperative Behavior Acquisition in Multi-Mobile Robots Environment by Reinforcement Learning Based on State Vector Estimation. In Proc. of IEEE International Conference on Robotics and Automation, pages 1558–1563, 1998.
E. Uchibe, M. Asada, and K. Hosoda. State Space Construction for Behavior Acquisition in Multi Agent Environments with Vision and Action. In Proc. of International Conference on Computer Vision, pages 870–875, 1998.
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© 1999 Springer-Verlag Berlin Heidelberg
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Uchibe, E., Nakamura, M., Asada, M. (1999). Cooperative Behavior Acquisition in a Multiple Mobile Robot Environment by Co-evolution. In: Asada, M., Kitano, H. (eds) RoboCup-98: Robot Soccer World Cup II. RoboCup 1998. Lecture Notes in Computer Science(), vol 1604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48422-1_22
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DOI: https://doi.org/10.1007/3-540-48422-1_22
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