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.
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
References
Aoki, I.: A simulation study on the schooling mechanism in fish. Bull. Jan. Soc. Sci. Fish. 8(8), 1081–1088 (1982)
Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., Lecomte, V., Orlandi, A., Parisi, G., Procaccini, A., Viale, M., Zdravkovic, V.: Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study. Proc. National Acad. Sci. USA 105(4), 1232–1237 (2008)
Makiguchi, M., Inoue, J.: Emergence of anisotropy in flock simulations and its computational analysis. Trans. Soc. Instrum. Control Eng. 46(11), 666–675 (2010). (in Japanese)
Morihiro, K., Isokawa, T., Nishimura, H., Tomimasu, M., Kamiura, N., Matsui, N.: Reinforcement learning scheme for flocking behavior emergence. J. Adv. Comput. Intell. Intell. Inf. 11(2), 155–161 (2007)
Morihiro, K., Nishimura, H., Isokawa, T., Matsui, N.: Grouping and anti-predator behaviors for multi-agent systems based on reinforcement learning scheme. In: Innovations in Multi-Agent Systems and Application-1, Chap. 6, pp. 149–183. Springer-Verlag, Berlin, Heidelberg (2010)
Reynolds, C.W.: Flocks, herds, and schools : a distributed behavioral model. ACM SIGGRAPH Comput. Graph. 21(4), 25–34 (1987)
Shimoyama, N., Sugawara, K., Mizuguchi, T., Hayakawa, Y., Sano, M.: Collective motion in a system of motile elements. Phys. Rev. Lett. 76(20), 3870–3873 (1996)
Vicsek, T., Czirok, A., Jacob, E.B., Cohen, I., Shochet, O.: Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75(6), 1226–1229 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Japan
About this chapter
Cite this chapter
Ueyama, A., Isokawa, T., Nishimura, H., Matsui, N. (2016). A Comparison of Grouping Behaviors on Rule-Based and Learning-Based Multi-agent Systems. In: Suzuki, Y., Hagiya, M. (eds) Recent Advances in Natural Computing. Mathematics for Industry, vol 14. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55429-5_3
Download citation
DOI: https://doi.org/10.1007/978-4-431-55429-5_3
Published:
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-55428-8
Online ISBN: 978-4-431-55429-5
eBook Packages: EngineeringEngineering (R0)