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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NS | 2024-05-09 14:15 |
Mie | Sinfonia Technology Hibiki Hall Ise (Primary: On-site, Secondary: Online) |
[Encouragement Talk]
Improvement on the Dueling Networks Architecture for GNN in the Deep Reinforcement Learning for the Automated ICT System Design Tianchen Zhou (Sophia Univ.), Yutaka Yakuwa (NEC), Natsuki Okamura, Hiroyuki Hochigai (Sophia Univ.), Takayuki Kuroda (NEC), Ikuko E. Yairi (Sophia Univ.)  NS2024-16 |
We have been addressing the challenge of low learning efficiency in automated ICT system design with reinforcement learn... [more] | NS2024-16 pp.17-22 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-03 17:00 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Multi-agent reinforcement learning based control method for large-scale crowd movement on Mojiko Fireworks Festival dataset Kazuya Miyazaki, Masato Kiyama, Motoki Amagasaki, Toshiaki Okamoto (Kumamoto Univ.) IBISML2023-45 |
The importance of human flow guidance is increasing in response to accidents at events. In recent years, some research h... [more] | IBISML2023-45 pp.36-43 |
| SS, MSS | 2024-01-17 14:30 |
Ishikawa | (Primary: On-site, Secondary: Online) |
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator Ryoma Furuyama, Daiki Kuyoshi, Yamane Satoshi (Kanazawa Univ.) MSS2023-55 SS2023-34 |
Imitation learning is often used in addition to reinforcement learning in environments where reward design is difficult ... [more] | MSS2023-55 SS2023-34 pp.19-24 |
| MSS, CAS, IPSJ-AL [detail] | 2023-11-16 16:30 |
Okinawa | Deep Reinforcement Learning for Multi-Agent Systems with Temporal Logic Specifications Keita Terashima, Koichi Kobayashi, Yuh Yamashita (Hokkaido Univ.) CAS2023-70 MSS2023-40 |
In multi-agent systems, the challenge is how a group of agents collaborate to achieve a common goal. In our previous wor... [more] | CAS2023-70 MSS2023-40 pp.54-58 |
|
| RISING (3rd) |
2023-10-31 13:00 |
Hokkaido | Kaderu 2・7 (Sapporo) | [Poster Presentation]
Wireless MAC Protocol Adaptation Method Considering Application Layer Koshiro Aruga, Takeo Fujii (UEC) |
In recent years, with the development of the Internet of Things (IoT), the number of devices performing wireless communi... [more] | |
| NC, MBE (Joint) |
2023-10-27 13:30 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
Significance of single cell recording
-- Reverse engineering from supplementary motor cortex neuronal activity to reinforcement learning model -- Nao Matsumoto, Naoki M. Tamura, Hajime Mushiake (Tohoku Univ. Sch. Med.), Kazuhiro Sakamoto (TMPU) NC2023-25 |
Elucidating the regions of the brain that are active in a given cognitive activity is an important mission in neuroscien... [more] | NC2023-25 pp.1-5 |
| CCS, IN (Joint) |
2023-08-03 11:09 |
Hokkaido | Banya-no-yu | Reinforcement learning-based control of CWmin and Carrier Sense Threshold for IEEE 802.11 WLAN. Yuto Higashiyama, Kosuke Sanada, Hiroyuki Hatano, Kazuo Mori (Mie Univ.) CCS2023-20 |
Distribute Coordination Function (DCF) is a basic channel access protocol in IEEE 802.11 Wireless Local Area Networks (W... [more] | CCS2023-20 pp.19-24 |
| IN, NS (Joint) |
2023-03-02 13:30 |
Okinawa | Okinawa Convention Centre + Online (Primary: On-site, Secondary: Online) |
Online Deep Reinforcement Learning for Network Slice Reconfiguration under Variable Number of Service Function Chains Kairi Tokuda, Takehiro Sato, Eiji Oki (Kyoto Univ.) NS2022-181 |
This paper proposes Deep reinforcement learning model for Network Slice Reconfiguration with Dummy and Partial greedy ex... [more] | NS2022-181 pp.83-88 |
| MBE, NC | 2022-12-03 14:45 |
Osaka | Osaka Electro-Communication University | Acquisition process of value evaluation criteria of a reinforcement learning agent with embodiment and intrinsic motivation in drawing Yoshia Abe, Shogo Yonekura, Yoshiyuki Ohmura, Yasuo Kuniyoshi (UTokyo) MBE2022-38 NC2022-60 |
A part of a human's aesthetic sense can be acquired through experience. However, little is known about the mechanism of ... [more] | MBE2022-38 NC2022-60 pp.74-79 |
| RISING (3rd) |
2022-10-31 13:00 |
Kyoto | Kyoto Terrsa (Day 1), and Online (Day 2, 3) | [Poster Presentation]
Learning-based Environment Adaptive Wireless MAC Protocol Design Koshiro Aruga, Takeo Fujii (UEC) |
With the Internet of Things (IoT) era, the number of devices that communicate wirelessly has been rapidly increasing. In... [more] | |
| NC, MBE (Joint) |
2022-09-30 11:35 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
A dynamic state-space reinforcement learning model explaining functional differentiation of higher motor areas in the cerebral cortex Naoki Tamura, Hajime Mushiake (Tohoku Univ), Kazuhiro Sakamoto (TMPU) NC2022-42 |
Complex and sequential behaviors based on various cues depend on the frontal higher motor areas of the cerebral cortex. ... [more] | NC2022-42 pp.44-48 |
| AI | 2022-07-04 10:20 |
Hokkaido | (Primary: On-site, Secondary: Online) |
Designing Rewards in Deep Reinforcement Learning for Chick Feeding System Masato Kijima, Katsuhide Fujita, Tsuyoshi Shimmura (TAT) AI2022-2 |
Livestock is raised cage-free, and there is an urgent need to develop an appropriate livestock management system. The... [more] |
AI2022-2 pp.7-12 |
| NS | 2022-04-15 11:10 |
Tokyo | kikai shinkou kaikan + online (Primary: On-site, Secondary: Online) |
Service Chaining Based on Capacitated Shortest Path Tour Problem
-- Solution Based on Deep Reinforcement Learning and Graph Neural Network -- Takanori Hara, Masahiro Sasabe (NAIST) NS2022-2 |
The service chaining problem is one of the resource allocation problems in network functions virtualization (NFV) networ... [more] | NS2022-2 pp.7-12 |
| MSS, NLP | 2022-03-29 10:05 |
Online | Online | Relationship between Computational Performance and Task Difficulty of Reinforcement Learning Methods Using Reward Machines Ryuji Watanabe, Gouhei Tanaka (The Univ. of Tokyo) MSS2021-70 NLP2021-141 |
In reinforcement learning, it is necessary to take into account the history of past state transitions during learning fo... [more] | MSS2021-70 NLP2021-141 pp.77-82 |
| CCS | 2022-03-27 17:05 |
Hokkaido | RUSUTSU RESORT HOTEL & CONVENTION (Primary: On-site, Secondary: Online) |
Reinforcement-learning based selection of CWmin in IEEE 802.11 networks. Kosuke Sanada (Mie Univ.) CCS2021-51 |
This paper proposes Q-learning based selection of contention window minimum value in IEEE 802.11 networks. In the propos... [more] | CCS2021-51 pp.90-95 |
| SS, MSS | 2022-01-11 14:05 |
Nagasaki | Nagasakiken-Kensetsu-Sogo-Kaikan Bldg. (Primary: On-site, Secondary: Online) |
Design of Self-Triggered Reduced-Order Controllers of Probabilistic Boolean Networks Using Reinforcement Learning Michiaki Takizawa, Koichi Kobayashi, Yuh Yamashita (Hokkaido Univ.) MSS2021-37 SS2021-24 |
We consider the stabilization of Probabilistic Boolean Networks using reinforcement learning. Using reinforcement learni... [more] | MSS2021-37 SS2021-24 pp.35-39 |
| SIP, CAS, VLD, MSS | 2021-07-06 11:15 |
Online | Online | Pinning Stabilization of Probabilistic Boolean Networks Using Reinforcement Learning Michiaki Takizawa, Koichi Kobayashi, Yuh Yamashita (Hokkaido Univ.) CAS2021-12 VLD2021-12 SIP2021-22 MSS2021-12 |
We consider the pinning stabilization of probabilistic Boolean networks using reinforcement learning. Using reinforcemen... [more] | CAS2021-12 VLD2021-12 SIP2021-22 MSS2021-12 pp.60-63 |
| IBISML | 2020-10-21 10:25 |
Online | Online | IBISML2020-19 |
Due to the decreasing birthrate and labor force, expectations are rising for the automatic operation of various robots a... [more] | IBISML2020-19 p.36 |
| IN, CCS (Joint) |
2020-08-04 10:00 |
Online | Online | [Invited Talk]
Reinforcement Learning Based Channel Selection Algorithm for IoT Devices and Its Application to Wireless Sensor Network for Building Monitoring System So Hasegawa (NICT), Ryoma Kitagawa, Takumi Ito, Takashi Nakajima (TUS), Song-Ju Kim (KU), Yoshito Watanabe, Yozo Shoji (NICT), Mikio Hasegawa (TUS) CCS2020-13 |
The IoT wave have spread and the number of IoT devices have rapidly increased. In IoT system using numerous IoT devices... [more] |
CCS2020-13 pp.5-10 |
| IE, IMQ, MVE, CQ (Joint) [detail] |
2020-03-05 09:20 |
Fukuoka | Kyushu Institute of Technology (Cancelled but technical report was issued) |
Self-Play Reinforcement Learning for Fast Image Retargeting Nobukatsu Kajiura, Satoshi Kosugi, Xueting Wang, Toshihiko Yamasaki, Kiyoharu Aizawa (UTokyo) IMQ2019-40 IE2019-122 MVE2019-61 |
We address image retargeting, which is a task of adjusting input images into arbitrary sizes. In a previous method, they... [more] | IMQ2019-40 IE2019-122 MVE2019-61 pp.127-131 |
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