A Comparison of Grouping Behaviors on Rule-Based and Learning-Based Multi-agent Systems

  • Akihiro Ueyama
  • Teijiro Isokawa
  • Haruhiko Nishimura
  • Nobuyuki Matsui
Chapter
Part of the Mathematics for Industry book series (MFI, volume 14)

Abstract

Grouping behavior, such as bird flocking, terrestrial animal herding, and fish schooling, is one of well-known emergent phenomena. Several models have been proposed for describing grouping behaviors, and two types of models can be defined: rule-based model and learning-based model. In rule-based models, each agent in a group has fixed interaction rules with respect to other agents. On the other hand, agents in learning-based model acquire their rules by the interactions of other agents with a learning scheme such as Q-learning. In this paper, we adopt quantities obtained from trails of agents, in order to investigate the properties for grouping behaviors of agents. We also evaluate rule-based and learning-based models by using these quantities under the environments with and without predatory agents.

Keywords

Multi-agent system Grouping behavior Q-learning Anisotropy 

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Copyright information

© Springer Japan 2016

Authors and Affiliations

  • Akihiro Ueyama
    • 1
  • Teijiro Isokawa
    • 1
  • Haruhiko Nishimura
    • 2
  • Nobuyuki Matsui
    • 1
  1. 1.Graduate School of EngineeringUniversity of HyogoHimejiJapan
  2. 2.Graduate School of Applied InformaticsUniversity of HyogoKobeJapan

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