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JRM Vol.35 No.4 pp. 1016-1027
doi: 10.20965/jrm.2023.p1016
(2023)

Paper:

Consensus Building in Box-Pushing Problem by BRT Agent that Votes with Frequency Proportional to Profit

Masao Kubo*, Hiroshi Sato* ORCID Icon, and Akihiro Yamaguchi**

*Department of Computer Science, National Defense Academy of Japan
1-10-20 Hashirimizu, Yokosuka, Kanagawa 239-8686, Japan

**Department of Information and Systems Engineering, Fukuoka Institute of Technology
3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka 811-0295, Japan

Received:
January 18, 2023
Accepted:
July 5, 2023
Published:
August 20, 2023
Keywords:
collective intelligence, swarm intelligence, voting model, reinforcement learning
Abstract

In this study, we added voting behavior in which voting proportionately reflects the value of a view (option, opinion, and so on) to the BRT agent. BRT agent is a consensus-building model of the decision-making process among a group of human, and is a framework that allows the expression of the collective behavior while maintaining dispersiveness, although it has been noted that it is unable to reach consensus by making use of experience. To resolve this issue, we propose the incorporation of a mechanism of voting at frequencies proportional to the value estimated using reinforcement learning. We conducted a series of computer-based experiments using the box-pushing problem and verified that the proposed method reached a consensus to arrive at solutions based on experience.

40 learning BRT agents for box-pushing

40 learning BRT agents for box-pushing

Cite this article as:
M. Kubo, H. Sato, and A. Yamaguchi, “Consensus Building in Box-Pushing Problem by BRT Agent that Votes with Frequency Proportional to Profit,” J. Robot. Mechatron., Vol.35 No.4, pp. 1016-1027, 2023.
Data files:
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Last updated on Jun. 03, 2024