Reinforcement learning scheme for grouping and anti-predator behavior

Authors:
Koichiro Morihiro;Haruhiko Nishimura;Teijiro Isokawa;Nobuyuki Matsui
Affiliations:
Hyogo University of Teacher Education, Hyogo, Japan and Himeji Institute of Technology, Hyogo, Japan;Graduate School of Applied Informatics, University of Hyogo, Hyogo, Japan;Himeji Institute of Technology, Hyogo, Japan and Graduate School of Engineering, University of Hyogo, Hyogo, Japan;Himeji Institute of Technology, Hyogo, Japan and Graduate School of Engineering, University of Hyogo, Hyogo, Japan
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Year:
2007

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Abstract

Collective behavior such as bird flocking, land animal herding, and fish schooling is well known in nature. Many observations have shown that there are no leaders to control the behavior of a group. Several models have been proposed for describing the grouping behavior, which we regard as a distinctive example of aggregate motions. In these models, a fixed rule is provided for each of the individuals a priori for their interactions in a reductive and rigid manner. In contrast, we propose a new framework for the self-organized grouping of agents by reinforcement learning. It is important to introduce a learning scheme for causing collective behavior in artificial autonomous distributed systems. The behavior of agents is demonstrated and evaluated through computer simulations and it is shown that their grouping and anti-predator behavior emerges as a result of learning.