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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| R | 2023-12-07 14:40 |
Tokyo | Kikai-Shinko-Kaikan Bldg (Primary: On-site, Secondary: Online) |
Optimal CBM policy for a degrading system with two weighted components Shinji Kawano, Nobuyuki Tamura (Hosei Univ.)  R2023-51 |
We consider a system in which the degradation of two components follows a Wiener process with different drifts, and a fa... [more] | R2023-51 pp.6-11 |
| AI | 2023-09-12 14:55 |
Hokkaido | Event-Driven Reinforcement Learning with Semi Markov Models for Stable Air-Conditioning Control Hayato Chujo, Arai Sachiyo (Chiba Univ) AI2023-16 |
This study deals with air conditioning control that optimizes room temperature by switching heaters on/off. The control ... [more] | AI2023-16 pp.83-86 |
|
| DC, SS | 2022-10-25 10:00 |
Fukushima | (Primary: On-site, Secondary: Online) |
A note on performance and sensitivity analysis of self-adaptive systems using parametric Markov decision processes Junjun Zheng, Hiroyuki Nakagawa, Tatsuhiro Tsuchiya (Osaka Univ.) SS2022-21 DC2022-27 |
This paper considers the sensitivity analysis for a self-adaptive system with uncertain parameters. The system behavior ... [more] | SS2022-21 DC2022-27 pp.1-5 |
| 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 |
| RCS, SR, SRW (Joint) |
2021-03-05 16:30 |
Online | Online | [Invited Lecture]
A Hazardous Spot Detection Framework by Mobile Sensing and V2V Opportunistic Networks Yoshito Watanabe, Yozo Shoji (NICT) SR2020-88 |
This study proposes a framework to detect hazardous spots on roads by combining mobile sensing on commercial-use vehicle... [more] | SR2020-88 pp.91-98 |
| IBISML | 2021-03-03 14:25 |
Online | Online | Markov Decision Processes for Simultaneous Control of Multiple Objects with Different State Transition Probabilities in Each Cluster Yuto Motomura, Akira Kamatsuka, Koki Kazama, Toshiyasu Matsushima (Waseda Univ.) IBISML2020-49 |
In this study, we propose an extended MDP model, which is a Markov decision process model with multiple control objects ... [more] | IBISML2020-49 pp.47-54 |
| IT | 2020-12-02 10:00 |
Online | Online | Policy Optimization Based on Bayesian Decision Theory in Learning Period on Markov Decision Process Naoki Ichijo, Yuta Nakahara, Yuto Motomura, Toshiyasu Matsushima (Waseda Univ.) IT2020-31 |
In Markov decision process(MDP) problems with an unknown transition probability, a learning agent has to learn the unkno... [more] | IT2020-31 pp.38-43 |
| SeMI, RCS, NS, SR, RCC (Joint) |
2019-07-10 16:45 |
Osaka | I-Site Nanba（Osaka） | [Invited Talk]
Current Status of Reinforcement Learning
-- Algorithms and Applications -- Shin-ichi Maeda (PFN) RCC2019-18 NS2019-51 RCS2019-108 SR2019-27 SeMI2019-27 |
Reinforcement Learning is a framework to optimize an action sequence in terms of the return maximization. In this talk, ... [more] | RCC2019-18 NS2019-51 RCS2019-108 SR2019-27 SeMI2019-27 p.39(RCC), p.49(NS), p.43(RCS), p.49(SR), p.53(SeMI) |
| SR | 2019-01-24 15:20 |
Fukushima | Corasse, Fukushima city (Fukushima prefecture) | An Error Correction Technique with the Viterbi Algorithm for a Machine-Learning-Based Classifier Yoshito Watanabe, Yozo Shoji (NICT) SR2018-105 |
This paper proposes a novel technique to correct errors that can be caused in the decisions made by a machine learning (... [more] | SR2018-105 pp.57-61 |
| CQ | 2017-01-19 14:40 |
Osaka | Osaka University Nakanoshima Center | Markov Decision Process Assisted User Multi-flow Mobile Data Offloading Cheng Zhang (Waseda Univ.), Bo Gu (Kogakuin Univ.), Zhi Liu (Waseda Univ.), Kyoko Yamori (Asahi Univ./Waseda Univ.), Yoshiaki Tanaka (Waseda Univ.) CQ2016-94 |
With rapid increases in demand for mobile data, mobile network operators are trying to expand wireless network capacity ... [more] | CQ2016-94 pp.25-30 |
| IBISML | 2016-11-17 14:00 |
Kyoto | Kyoto Univ. | Approximate Value Iteration Algorithms for Partially Observable Markov Decision Processes in Geometric Dual Representation Hiroshi Tsukahara, Mitsuru Anbai, Makoto Oobayashi (Denso IT Lab.) IBISML2016-71 |
We propose new approximate algorithms for the value iteration of partially observable Markov decision processes (POMDPs... [more] |
IBISML2016-71 pp.177-184 |
| SIS | 2016-06-09 11:20 |
Hokkaido | Kushiro Tourism and Convention cent. | A Note on Teaching Strategies Using Markov Decision Processes Yasunari Maeda, Masakiyo Suzuki (KIT) SIS2016-2 |
In this research Markov decision processes(MDP) with unknown states are used in order to represent lectures. Effectivene... [more] | SIS2016-2 pp.7-10 |
| NS, IN (Joint) |
2016-03-03 10:40 |
Miyazaki | Phoenix Seagaia Resort | Message Forwarding Method based on Markov Decision Process in Energy Harvesting Delay Tolerant Networks with Unmanned Aerial Vehicles Kyohei Kinoshita, Takuji Tachibana (Univ. of Fukui) NS2015-184 |
In energy harvesting delay tolerant networks (DTN), each node carries, stores, and forwards messages to other nodes whil... [more] | NS2015-184 pp.95-98 |
| IT | 2014-07-17 15:15 |
Hyogo | Kobe University | A Note on Optimal Control System for Selective-Repeat Hybrid SR-ARQ with a Finite Length Buffer Yuta Kageyama, Akira Kamatsuka (Waseda Univ.), Yasunari Maeda (Kitami Institute of Technology), Toshiyasu Matsushima (Waseda Univ.) IT2014-21 |
Hybrid ARQ is one of the error correction scheme．It is Combination of ARQ and FEC．Algorithms for throughput maximization... [more] | IT2014-21 pp.55-58 |
| CAS, SIP, MSS, VLD, SIS [detail] | 2014-07-10 14:45 |
Hokkaido | Hokkaido University | [Tutorial Lecture]
Markov Decision Processes and Its Applications Yasunari Maeda, Masakiyo Suzuki (Kitami Inst. of Tech.) CAS2014-31 VLD2014-40 SIP2014-52 MSS2014-31 SIS2014-31 |
There are many research on Markov decision processes in the areas of operations research and artificial intelligence. Th... [more] | CAS2014-31 VLD2014-40 SIP2014-52 MSS2014-31 SIS2014-31 pp.163-168 |
| SP, IPSJ-MUS | 2014-05-25 11:30 |
Tokyo | Text-to-speech prosody synthesis based on probabilistic model for F0 contour Kento Kadowaki, Tatsuma Ishihara, Nobukatsu Hojo (Univ. of Tokyo), Hirokazu Kameoka (Univ. of Tokyo/NTT) SP2014-28 |
This paper deals with the problem of generating the fundamental frequency (F0) contour of speech from a text input for t... [more] | SP2014-28 pp.309-314 |
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| NC, NLP | 2013-01-24 10:10 |
Hokkaido | Hokkaido University Centennial Memory Hall | Analysis of Medical Treatment Data using Inverse Reinforcement Learning Hideki Asoh, Masanori Shiro, Toshihiro Kamishima, Shotaro Akaho (AIST), Takahide Kohro (Univ. of Tokyo Hospital) NLP2012-106 NC2012-96 |
It is an important issue to utilize large amount of medical records which are accumulated on medical information systems... [more] | NLP2012-106 NC2012-96 pp.13-17 |
| NC, NLP | 2013-01-24 10:50 |
Hokkaido | Hokkaido University Centennial Memory Hall | Significance of non-stationary of dynamics for learning cooperative behavior Akihiro Tawa, Shin-ichi Maeda, Shin Ishii (Kyoto Univ.) NLP2012-108 NC2012-98 |
To understand how cooperative behaviors emerge is important in the field of multi-agent system research. Although this e... [more] | NLP2012-108 NC2012-98 pp.25-30 |
| IBISML | 2012-11-07 15:30 |
Tokyo | Bunkyo School Building, Tokyo Campus, Tsukuba Univ. | Robustness of time-consistent Markov decision processes Takayuki Osogami (IBM Japan) IBISML2012-40 |
We show that an optimal policy for a Markov decision process (MDP) can be found with dynamic programming, when the objec... [more] | IBISML2012-40 pp.45-52 |
| SR, AN, USN, RCS (Joint) |
2012-10-19 14:55 |
Fukuoka | Fukuoka univ. | A Fuzzy Q-Learning Based Sensing Policy for Cognitive Radio Systems Fereidoun H. Panahi, Tomoaki Ohtsuki (Keio Univ.) RCS2012-155 |
In a cognitive radio (CR) network, the channel sensing scheme to detect the appearance of a primary user (PU) directly a... [more] | RCS2012-155 pp.173-178 |
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