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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IA | 2024-01-25 16:40 |
Tokyo | Kwansei Gakuin Univiversity, Marunouchi Campus (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Study on Routing Schemes Using Reinforcement Learning with Cooperation for IoT Applications Takahashi Shotaro, Inoue Shota, Ohsaki Hiroyuki (Kwansei Univ)  IA2023-71 |
Low-power and Lossy Networks (LLN) have attracted much attention as wireless networks for Internet of Things (IoT) appli... [more] | IA2023-71 pp.59-64 |
| SR | 2022-01-25 14:40 |
Online | Online | Performance Evaluation of Access Control and Transmission Datarate Adaptation using Redundant Check Information for IEEE 802.11ax Wireless LAN Kazuto Yano, Kenta Suzuki, Babatunde Ojetunde (ATR), Koji Yamamoto (Kyoto Univ.) SR2021-81 |
In order to meet increasing traffic load on wireless communication, the authors have conducted research and development ... [more] | SR2021-81 pp.103-110 |
| IN, IA (Joint) |
2021-12-17 18:10 |
Hiroshima | Higashi-Senda campus, Hiroshima Univ. (Primary: On-site, Secondary: Online) |
[Short Paper]
Study on Improving the Characteristics of Random Walk on Graph using Q-learning Tomoyuki Miyashita, Taisei Suzuki, Ryotaro Matsuo, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2021-51 |
In recent years, modeling mobile agent on unknown graphs, such as random walks on graphs and understanding its mathemati... [more] | IA2021-51 pp.100-103 |
| MBE, NC (Joint) |
2021-10-28 16:20 |
Online | Online | Study on rounding error and Learning performance of reinforcement learning model for FPGA implementation Daisuke Oguchi, Satoshi Moriya, Hideaki Yamamoto, Shigeo Sato (Tohoku Univ) NC2021-24 |
In recent years, the hardware implementation of reinforcement learning (RL) has attracted attention due to its wide rang... [more] | NC2021-24 pp.34-39 |
| CQ, MIKA (Joint) |
2021-09-09 10:05 |
Online | Online | Load balancing method using reinforcement learning between edge and cloud Hiroki Kobari, Zhaoyang Du, Celimuge Wu, Tsutomu Yoshinaga (UEC) CQ2021-38 |
Recently, edge computing has attracted more and more attention. Compared with traditional cloud computing, edge computin... [more] | CQ2021-38 pp.6-10 |
| RCS | 2019-06-21 11:10 |
Okinawa | Miyakojima Hirara Port Terminal Building | Interference Control of LTE-LAA using Q-learning with HARQ Kenshiro Wada, Tomoaki Ohtsuki (Keio Univ.) RCS2019-91 |
As a means of high-speed and large-capacity communication, broadbandization of LTE communication is considered. Howeve... [more] |
RCS2019-91 pp.315-320 |
| CS, CQ (Joint) |
2019-04-19 10:25 |
Osaka | Osaka Univ. Library | Introduction of Q-Learning to Fixed Assignment based Window Access Scheme with Capture(FAWAC) Yuki Uehara, Megumi Saito, Shigeru Shimamoto (Waseda Univ.) CS2019-10 |
This paper presents the effects of applying Q-learning to fixed assignment based window access scheme with capture, such... [more] | CS2019-10 pp.51-54 |
| RCS, SR, SRW (Joint) |
2019-03-08 09:50 |
Kanagawa | YRP | [Invited Lecture]
A Reinforcement Learning Framework for User-to-Access Points Association in Future Wireless Networks Megumi Kaneko, Thi Ha Ly Dinh (NII), Keisuke Wakao, Hirantha Abeysekera, Yasushi Takatori (NTT) RCS2018-322 |
This work investigates the issue of distributed user-to-multiple Acess Points (AP) association, where a user requiring s... [more] | RCS2018-322 p.205 |
| SR | 2018-10-30 10:30 |
Overseas | Mandarin Hotel, Bangkok, Thailand | [Poster Presentation]
Q-Leaning based Cell Zooming for Energy-Harvesting Small Cell Networks Katsuya Suto (UEC), Masashi Wakaiki (Kobe Univ.) SR2018-62 |
With the dense deployment of small cell base stations (SBSs), the radio access network can boost spectrum efficiency. To... [more] | SR2018-62 pp.9-10 |
| SR, RCS (Joint) (2nd) |
2018-10-30 10:30 |
Overseas | Mandarin Hotel, Bangkok, Thailand | [Poster Presentation]
Multi-agent Trust Evaluation in Vehicular Internet of Things Celimuge Wu, Tsutomu Yoshinaga (UEC), Yusheng Ji (NII) |
We propose a decentralized trust management scheme for vehicular networks. The proposed scheme uses a fuzzy logic-based ... [more] | |
| SAT, WBS (Joint) |
2018-05-25 09:30 |
Kagoshima | Kagoshima University | A Study on Resource Allocation Utilizing Q-Learning for Video Transmission in Multiple UAS Networks Saki Tashiro, Yuichi Kawamoto, Hiroki Nishiyama, Nei Kato (Tohoku Univ.) SAT2018-7 |
In recent years, the number of the use of unmanned aircrafts(UAs) has increased remarkably, and the world market size of... [more] | SAT2018-7 pp.31-36 |
| MoNA | 2018-01-19 11:15 |
Kyoto | Campus Plaza Kyoto | Decentralized WLAN Access Point Selection through Reinforcement Learning Takuya Nakamura, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ.), Toshihisa Nabetani (TOSHIBA) MoNA2017-51 |
Many operators provide public wireless LAN services in public places such as stations or cafes. In many cases, a station... [more] | MoNA2017-51 pp.57-62 |
| NLC, IPSJ-NL, SP, IPSJ-SLP (Joint) [detail] |
2017-12-20 17:20 |
Tokyo | Waseda Univ. Green Computing Systems Research Organization | Trial of Reinforcement Learning to Analyze Command Utterances for In-vehicle Computer Masato Tokuhisa, Shuhei Kimura (Tottori Univ.) NLC2017-38 |
This paper reports a trial of reinforcement learning to analyze command utterances for an in-vehicle computer. The compu... [more] | NLC2017-38 pp.55-60 |
| AI | 2016-12-09 16:40 |
Oita | Applying reinforcement learning to multi-agent patrolling with priority setting Harunobu Ozeki, Ayumi Sugiyama, Toshiharu Sugawara (Waseda Univ.) AI2016-21 |
We propose a multi-agent patrolling method in an environment with priority. Cooperative behavior is required that a team... [more] | AI2016-21 pp.49-54 |
|
| AI | 2016-12-09 17:05 |
Oita | Reinforcement Learning using Filter and Coarse-Grained States in Multiagent Exploration Problems Takahisa Yutoku, Ayumi Sugiyama, Toshiharu Sugawara (Waseda Univ.) AI2016-22 |
(To be available after the conference date) [more] | AI2016-22 pp.55-60 |
|
| AI, JSAI-KBS, JSAI-DOCMAS, JSAI-SAI, IPSJ-ICS | 2016-03-01 - 2016-03-04 |
Hokkaido | Efficient distributed search method using Q-learning in an environment with map Takahisa Yutoku, Ayumi Sugiyama, Toshiharu Sugawara (waseda) AI2015-65 |
We propose a method for learning cooperative behavior efficiently in a multi-agent system running in an large environmen... [more] | AI2015-65 pp.1-6 |
|
| NC, MBE (Joint) |
2014-03-18 13:20 |
Tokyo | Tamagawa University | Acquisition of Cooperative and Competitive Behaviors in a Two-Players Soccer Game Takao Satou, Kiyoshi Nishiyama (Iwate Univ.) NC2013-137 |
The acquisition of an autonomous agent's action rule is a very interesting topic in the field of machine learning. The s... [more] | NC2013-137 pp.281-286 |
| RCS, SR, SRW (Joint) |
2014-03-03 15:10 |
Tokyo | Waseda Univ. | Combined Learning Based Cell Selection and Transmit Power Reduction in Heterogeneous Networks Toshihito Kudo, Tomoaki Ohtsuki (Keio Univ.) RCS2013-328 |
Cell range expansion (CRE) expands a pico cell range virtually with bias values, which can make cell edge throughput and... [more] | RCS2013-328 pp.133-138 |
| RCS, SIP | 2014-01-24 15:15 |
Fukuoka | Kyushu Univ. | UE Outage Reduction with Distributed Learning-based Cell Selection in Cell Range Expansion Toshihito Kudo, Tomoaki Ohtsuki (Keio Univ.) SIP2013-133 RCS2013-303 |
Cell range expansion (CRE) expands a pico cell range virtually by adding a bias value to the pico received power, instea... [more] | SIP2013-133 RCS2013-303 pp.281-286 |
| RCS | 2013-10-18 14:00 |
Tokyo | Sophia Univ. | Q-Learning Based Cell Selection in Heterogeneous Networks Toshihito Kudo, Tomoaki Ohtsuki (Keio Univ.) RCS2013-167 |
Cell range expansion (CRE) expands a pico cell range virtually by adding a bias value to the pico received power, instea... [more] | RCS2013-167 pp.145-150 |
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