| Presentation | 2023-03-03 Study on the resilient role in coordinated behavior of a triad using deep reinforcement learning and rule-based modeling Jun Ichikawa, Kazushi Tsutsui, Keisuke Fujii, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Group can often implement a task, which is difficult to do alone, or achieve higher performance than an individual. For such coordination, adjustments based on two types of information processing are crucial; (1) bottom-up of obtaining and using other members' states through haptic and visual senses and (2) top-down based on a group goal, task limitations, and a role. However, coordinated group behavior is not fully investigated from both processing. Meanwhile, our previous study conducted the behavioral experiment; triads repeatedly engaged in a coordinated drawing task where participants changed tensions by reels, shared heterogeneous roles, and moved a pen connected to the three threads to draw an equilateral triangle. The results suggested that a resilient role, which adjusted to help others and improve situations, was related to performance. In such a role, the above-mentioned processing would work; this study investigated the resilient role in the task using deep reinforcement learning and rule-based modeling. The simulation results indicated that the condition, in which an agent in this role acted by bottom-up and top-down processing, achieved higher performance (smaller pen deviation) than the only rule-based and random conditions. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | coordinated group behavior / role sharing / resilience / deep reinforcement learning / rule-based modeling |
| Paper # | HCS2022-93 |
| Date of Issue | 2023-02-23 (HCS) |
| Conference Information | |
| Committee | HCS |
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| Conference Date | 2023/3/2(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tokoha University（KusanagiCampus） |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Interaction modeling, etc. |
| Chair | Tomoko Kanda(Osaka Inst. of Tech.) |
| Vice Chair | Yugo Hayashi(Ritsumeikan Univ.) / Masashi Komori(Osaka Electro-Comm. Univ.) |
| Secretary | Yugo Hayashi(Nihon Univ.) / Masashi Komori(Kanagawa Univ.) |
| Assistant | HUANG HUNGHSUAN(Univ. of Fukuchiyama) / Jun Ichikawa(Shizuoka Univ.) / Kazuki Takashima(Tohoku Univ.) / Hiroto Saito(Tokyo Denki Univ.) / Ryo Ishii(NTT) / Yoshimasa Ohmoto(Shizuoka Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Human Communication Science |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Study on the resilient role in coordinated behavior of a triad using deep reinforcement learning and rule-based modeling |
| Sub Title (in English) | |
| Keyword(1) | coordinated group behavior |
| Keyword(2) | role sharing |
| Keyword(3) | resilience |
| Keyword(4) | deep reinforcement learning |
| Keyword(5) | rule-based modeling |
| 1st Author's Name | Jun Ichikawa |
| 1st Author's Affiliation | Shizuoka University(Shizuoka Univ.) |
| 2nd Author's Name | Kazushi Tsutsui |
| 2nd Author's Affiliation | Nagoya University(Nagoya Univ.) |
| 3rd Author's Name | Keisuke Fujii |
| 3rd Author's Affiliation | Nagoya University(Nagoya Univ.) |
| Date | 2023-03-03 |
| Paper # | HCS2022-93 |
| Volume (vol) | vol.122 |
| Number (no) | HCS-413 |
| Page | pp.pp.100-105(HCS), |
| #Pages | 6 |
| Date of Issue | 2023-02-23 (HCS) |