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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 21 - 40 of 57 [Previous]  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
MBE, NC, NLP, CAS
(Joint) [detail]
2020-10-30
13:30
Online Online On parameter identifications of Higher-Order IIR adaptive filters by using discrete chaotic systems
Yutaro Arai, Yasunori Sugita, Tadashi Tsubone (NUT) CAS2020-15 NLP2020-36
Since the structure of the error function is often multimodal, in case of a strategy that minimizes the error function i... [more] CAS2020-15 NLP2020-36
pp.21-24
MSS, NLP
(Joint)
2020-03-09
10:15
Aichi  
(Cancelled but technical report was issued)
A projected consensus-based algorithm for minimizing the maximum error of a system of linear equations with nonnegativity constraints
Kosuke Kawashima, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NLP2019-116
If a system of linear equations with nonnegativity constraints has a solution then it can be considered as a constrained... [more] NLP2019-116
pp.19-23
ICTSSL, IN 2019-10-17
10:50
Oita   Comparison of Efficiency between Fixed and Mobile Disaster Information Transmitters
Naoki Kobayashi, Yamazaki Tatsuya (Niigata Univ.), Sato Shosuke (Tohoku Univ.) ICTSSL2019-18 IN2019-32
To promote evacuation at the time of disaster, appropriate local information needs to be certainly transmitted to the ev... [more] ICTSSL2019-18 IN2019-32
pp.11-16
CQ 2019-08-28
16:10
Hokkaido Hakodate arena Optimal spreading of disaster warnings using VANETs -- Importance of quick sharing of evacuation information based on the experience of the Great East Japan Earthquake --
Kota Ichikawa, Alberto Gallegos, Taku Noguchi (Ritsumeikan Univ.) CQ2019-87
In recent years, the need of Vehicular Ad-hoc Networks (VANETs) during disasters has increased. For example, during the ... [more] CQ2019-87
pp.153-157
NLP, MSS
(Joint)
2019-03-14
14:20
Fukui Bunkyo Camp., Univ. of Fukui Improvement of Near-miss Courses by Reinforcement Learning to Search Ships' Courses
Takahiro Tomihara, Takeshi Kamio (Hiroshima City Univ.), Takahiro Tanaka (Japan Coast Guard Academy), Kunihiko Mitsubori (Takusyoku Univ.), Hisato Fujisaka (Hiroshima City Univ.) NLP2018-127
Deciding efficient and safe courses of ships before actual navigation is very important. We have developed multi-agent r... [more] NLP2018-127
pp.17-22
CAS, SIP, MSS, VLD 2018-06-14
10:00
Hokkaido Hokkaido Univ. (Frontier Research in Applied Sciences Build.) Self-Triggered Predictive Pinning Control for Consensus of Multi-Agent Systems
Shun Andoh, Koichi Kobayashi, Yuh Yamashita (Hokkaido Univ.) CAS2018-4 VLD2018-7 SIP2018-24 MSS2018-4
Pinning control of multi-agent systems is a method that the external control input is added to some agents (pinning node... [more] CAS2018-4 VLD2018-7 SIP2018-24 MSS2018-4
pp.17-21
MSS, NLP
(Joint)
2018-03-12
14:00
Osaka   Learning in Two-Player Matrix Games by Policy Gradient Lagging Anchor
Shiyao Ding, Toshimitsu Ushio (Osaka Univ.) MSS2017-79
We propose a novel multi-agent reinforcement learning (MARL) algorithm which is called a policy gra-
dient lagging anch... [more]
MSS2017-79
pp.11-14
MSS, NLP
(Joint)
2018-03-14
10:45
Osaka   Consensus Speed of Multi-agent Systems Modeled by Scale-free Graph
Akinori Sakaguchi, Toshimitsu Ushio (Osaka Univ.) NLP2017-109
In a pinning consensus problem of multi-agent systems, a consensus speed of a static pinning control is generally bounde... [more] NLP2017-109
pp.43-46
ITS, WBS, RCC 2017-12-15
11:45
Okinawa Tiruru/Okinawa Jichikaikan Predictive Pinning Control for Consensus of Multi-Agent Systems
Koichi Kobayashi (Hokkaido Univ.) WBS2017-72 ITS2017-49 RCC2017-88
In this paper, based on the policy of model predictive control (MPC), a new method of predictive pinning control is prop... [more] WBS2017-72 ITS2017-49 RCC2017-88
pp.209-213
SITE, IPSJ-CE, IPSJ-CLE 2017-12-08
15:40
Ehime   A Study of Diffusion of Virtual Currency -- Observations from Multi-Agent Simulation --
Shiro Uesugi (Matsuyama Junior Col.) SITE2017-59
This study presents an observation from simulated models of Diffusion of Virtual Currency using artisoc4.0 MAS. Virtual... [more] SITE2017-59
pp.23-26
ICTSSL, IN
(Joint)
2017-10-10
14:25
Shizuoka   A Proposal of Flexible IoT Edge Computing Architecture and An Application to Energy Management
Tadashi Ogino (Meisei Univ.), Shinji Kitagami (Waseda Univ.), Takuo Suganuma (Tohoku Univ.), Norio Shiratori (Chuo Univ.) IN2017-35
On a large-scale IoT system based on cloud computing, problems such as increase of network load, delay in response, inva... [more] IN2017-35
pp.1-6
NLP, CAS 2017-10-06
13:25
Niigata Machinaka Campus Nagaoka Influence of Updating Reference Courses on Reinforcement Learning to Search Ships' Courses
Takeshi Kamio (Hiroshima City Univ.), Takahiro Tanaka (Japan Coast Guard Academy), Kunihiko Mitsubori (Takushoku Univ.), Hisato Fujisaka (Hiroshima City Univ.) CAS2017-38 NLP2017-63
Deciding efficient and safe courses of ships before actual navigation is very important. We have developed multi-agent r... [more] CAS2017-38 NLP2017-63
pp.75-80
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2017-06-23
14:25
Okinawa Okinawa Institute of Science and Technology Improvement of The Success Rate of Communication based on Implicit Feedbacks
Yuki Fumoto (Computer Mind), Takashi Sato (NIT, Okinawa College) NC2017-5
There are two types problems that make communication difficult to establish; one is that concepts formed in each individ... [more] NC2017-5
pp.1-8
ICTSSL 2017-05-30
10:25
Miyagi NEXCO EAST TOHOKU Multiagent-based Evacuation Guidance Support System for Coastal Area
Hideyuki Takahashi, Kenta Katayama, Nobuhide Yokota, Kazuya Sugiyasu, Tetsuo Kinoshita (Tohoku Univ.) ICTSSL2017-8
It is difficult for us to deal with an unexpected natural disaster (e.g., earthquake, tsunami, torrential rain) which is... [more] ICTSSL2017-8
pp.35-39
NLP 2017-03-14
10:50
Aomori Nebuta Museum Warasse Influence of Reference Courses on Reinforcement Learning to Search Ships' Courses
Takeshi Kamio, Shunsuke Yasui (Hiroshima City Univ.), Takahiro Tanaka (Japan Coast Guard Academy), Kunihiko Mitsubori (Takushoku Univ.), Hisato Fujisaka (Hiroshima City Univ.) NLP2016-108
Deciding efficient and safe courses of ships before actual navigation is very important. We have developed multi-agent r... [more] NLP2016-108
pp.13-18
MSS, SS 2017-01-27
15:20
Kyoto Kyoto Institute of Technology A Study on Effect of Zeros in Pinning Consensus of Multi-agent Systems
Akinori Sakaguchi, Toshimitsu Ushio (Osaka Univ.) MSS2016-77 SS2016-56
In this study, we consider pinning control of a single agent for multi-agent systems. For multi-agent systems modeled by... [more] MSS2016-77 SS2016-56
pp.117-122
WBS, RCC, ITS 2016-12-02
13:00
Kagawa Sunport Hall Takamatsu Influence of communication range and packet collision on consensus control of multi-agent system
Shunsuke Noro, Kentaro Kobayashi, Hiraku Okada, Masaaki Katayama (Nagoya Univ.) WBS2016-73 ITS2016-40 RCC2016-62
This paper deals with consensus control for multi-agent systems. When agents exchange their information by wireless comm... [more] WBS2016-73 ITS2016-40 RCC2016-62
pp.149-154
RCC 2016-01-29
13:30
Osaka Kansai University Umekita Laboratory [Invited Talk] Distributed Cooperative Control for Multi-Agent Systems
Naoki Hayashi (Osaka Univ) RCC2015-90
Recently, cooperative control of multi-agent systems has attracted much attention in control engineering. This paper giv... [more] RCC2015-90
pp.23-24
CAS 2016-01-29
10:00
Tokyo Kikai-Shinko-Kaikan Bldg. Power interchange modeling using multi-agent system
Kentaro Tani (Grad. Inst. Entrepreneurial Studies), Yoshinobu Maeda, Keisuke Nakano (Niigata Univ.), Hideho Numata, Masakazu Sengoku (Grad. Inst. Entrepreneurial Studies) CAS2015-71
In recent year, photovoltaic systems that be becoming pervasive are turning point in power networks because the common h... [more] CAS2015-71
pp.57-60
NLP 2015-03-04
13:00
Hyogo Hyogo Prefecture Citizens' Hall The study of the evolution of cooperation due to the introduction of peer-punishment depending on the difference of payoffs
Tetsushi Ohdaira (Aoyama Gakuin Univ.) NLP2014-154
There are two types of punishment, i.e. peer-punishment that a player punishes an uncooperative player by paying certain... [more] NLP2014-154
pp.63-68
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