Abstract
In our cooperative behavior, there are two strategies: a passive behavioral strategy based on others’ behaviors and an active behavioral strategy based on the objective-first. However, it is not clear how to acquire a meta-strategy to switch those strategies. The purpose of the proposed study is to create agents with the meta-strategy and to enable complex behavioral choices with a high degree of coordination. In this study, we have experimented by using multi-agent collision avoidance simulations as an example of cooperative tasks. In the experiments, we have used reinforcement learning to obtain an active strategy and a passive strategy by rewarding the interaction with agents facing each other. Furthermore, we have examined and verified the meta-strategy in situations with opponent’s strategy switched.
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
Yokoyama, A., Omori, T.: Modeling of human intention estimation process in social interaction scene. In: 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6 (2010)
Miyamoto, K., Takefuji, Y., Watanabe, N.: Pedestrian meta-strategy analysis of collision avoidance with two autonomous agents. In: 2015 IEEE 4th Global Conference on Consumer Electronics(GCCE 2015), pp. 467–469 (2016)
Sugahara, R., Katagami, D.: Proposal of discommunication robot. In: The First International Conference on Human-Agent Interaction, I-2-2 (2013)
Katagami, D., Tanaka, Y.: Change of impression resulting from voice in discommunication motion of baby robot. In: HAI Symposium 2015, pp. 171–176 (2015). (in Japanese)
Kozima, H., Michalowski, M.P., Nakagawa, C.: Keepon. Int. J. Soc. Robot. 1(1), 3–18 (2009). https://doi.org/10.1007/s12369-008-0009-8
Sato, T.: Emergence of robust cooperative states by Iterative internalizations of opponents’ personalized values in minority game. J. Inf. Commun. Eng. (JICE) 3(5), 157–166 (2017)
Kitamura, Y., Tanaka, T., Kishino, F., Yachida, M.: Real-time path planning in a dynamically changing 3-D environment. In: Proceedings of International Conference on Intelligent Robots and Systems, Osaka, pp. 925–931 (1996)
Kerr, W., Spears, D., Spears, W., Thayer, D.: Two formal gas models for multi-agent sweeping and obstacle avoidance. In: Hinchey, M.G., Rash, J.L., Truszkowski, W.F., Rouff, C.A. (eds.) FAABS 2004. LNCS (LNAI), vol. 3228, pp. 111–130. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30960-4_8
Mastellone, S., Stipanović, D.M., Graunke, C.R., Intlekofer, K.A., Spong, M.W.: Formation control and collision avoidance for multi-agent non-holonomic systems: theory and experiments. Int. J. Robot. Res. 27(1), 107–126 (2008)
Thompson, S., Horiuchi, T., Kagami, S.: A probabilistic model of human motion and navigation intent for mobile robot path planning. In: Proceedings of IEEE International Conference on Autonomous Robots and Agents, vol. 77, no. 775, pp. 1051–1061 (2009)
Hamasaki, S., Tamura, Y., Yamashita, A., Asama, H.: Prediction of human’s movement for collision avoidance of mobile robot. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics, pp. 1633–1638 (2011)
Yamada, K., Takano, S., Watanabe, S.: Reinforcement learning approaches for acquiring conflict avoidance behaviors in multi-agent systems. In: 2011 IEEE/SICE International Symposium on System Integration, pp. 679–684 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Watanabe, N., Miyamoto, K. (2021). Agent Simulation of Collision Avoidance Based on Meta-strategy Model. In: Uchiya, T., Bai, Q., Marsá Maestre, I. (eds) PRIMA 2020: Principles and Practice of Multi-Agent Systems. PRIMA 2020. Lecture Notes in Computer Science(), vol 12568. Springer, Cham. https://doi.org/10.1007/978-3-030-69322-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-69322-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69321-3
Online ISBN: 978-3-030-69322-0
eBook Packages: Computer ScienceComputer Science (R0)