Learning Grouping and Anti-predator Behaviors for Multi-agent Systems

Authors:
Koichiro Morihiro;Haruhiko Nishimura;Teijiro Isokawa;Nobuyuki Matsui
Affiliations:
Hyogo University of Teacher Education, Hyogo, Japan 673-1494 and Himeji Institute of Technology, , Hyogo, Japan 671-2201;Graduate School of Applied Informatics, University of Hyogo, Hyogo, Japan 650-0044;Himeji Institute of Technology, , Hyogo, Japan 671-2201 and Graduate School of Engineering, University of Hyogo, Hyogo, Japan 671-2201;Himeji Institute of Technology, , Hyogo, Japan 671-2201 and Graduate School of Engineering, University of Hyogo, Hyogo, Japan 671-2201
Venue:
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Year:
2008

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Abstract

Several models have been proposed for describing grouping behavior such as bird flocking, terrestrial animal herding, and fish schooling. In these models, a fixed rule has been imposed on each individual a priori for its interactions in a reductive and rigid manner. We have proposed a new framework for self-organized grouping of agents by reinforcement learning. It is important to introduce a learning scheme for developing collective behavior in artificial autonomous distributed systems. This scheme can be expanded to cases in which predators are present. In this study we integrate grouping and anti-predator behaviors into our proposed scheme. The behavior of agents is demonstrated and evaluated in detail through computer simulations, and their grouping and anti-predator behaviors developed as a result of learning are shown to be diverse and robust by changing some parameters of the scheme.