国際会議Proceedings - 倉重 健太郎

分割表示 >> /  全件表示  56 件中 1 - 56 件目
  1. Proposal of Self-generation of Reward for danger avoidance by disregarding specific situations

    Yuya Ono, Kentaro Kurashige, Afiqe Anuar Bin Muhammad Nor Hakim, Sosuke Kondo, Kodai Fukuzawa,2021 IEEE Symposium Series on Computational Intelligence (SSCI),(頁 1 ~ 6),2021年12月,Orlando, FL, USA

  2. Efficient exploration by switching agents according to degree of convergence of learning on Heterogeneous Multi-Agent Reinforcement Learning in Single Robot

    Riku Narita, Tatsufumi Matsushima, Kentarou Kurashige,2021 IEEE Symposium Series on Computational Intelligence (SSCI),(頁 1 ~ 6),IEEE,2021年12月,Orlando, FL, USA

  3. An Experimental Study for Tracking Ability of Deep Q-Network under the Multi-Objective Behaviour using a Mobile Robot with LiDAR

    Masashi Sugimoto and Ryunosuke Uchida and Shinji Tsuzuki and Hitoshi Sori and Hiroyuki Inoue and Kentarou Kurashige and Shiro Urushihara,2021 International Symposium on Electrical, Electronics and Information Engineering(ISEEIE2021),(頁 81 ~ 87),ACM,2021年02月,Seoul Republic of Korea

  4. An Experimental Study for Tracking Ability of Deep Q-Network under the Multi-Objective Behaviour using a Mobile Robot with LiDAR

    Masashi Sugimoto, Ryunosuke Uchida, Shinji Tsuzuki, Hitoshi Sori, Hiroyuki Inoue, Kentarou Kurashige, Shiro Urushihara,2021 International Symposium on Electrical, Electronics and Information Engineering(ISEEIE2021),(頁 81 ~ 87),2021年02月,Seoul Republic of Korea

  5. An Experimental Study for Development of Multi-Objective Deep Q-Network -In Case of Behavior Algorithm for Resident Tracking Robot System-

    Masashi Sugimoto, Ryunosuke Uchida, Haruka Matsufuji, Shinji Tsuzuki, Hitoshi Yoshimura, Kentarou Kurashige, Mikio Deguchi,Proceedings of the Sixth International Conference on Electronics and Software Science ICESS2020,(頁 7 ~ 16),2020年12月,Japan

  6. Autonomous decision making by the self-generated priority under multi-task

    Takuma Kambayashi, Kentarou Kurashige,Proceedings of 2020 IEEE Symposium Series on Computational Intelligence (SSCI),(頁 1879 ~ 1885),IEEE,2020年12月,Canberra, Australia

  7. Self-generation of reward based on sensor value -Improving reward accuracy by associating multiple sensors using Hebb's rule-

    Sosuke Kondo, Kentarou Kurashige,Proceedings of 2020 IEEE Symposium Series on Computational Intelligence (SSCI),(頁 1886 ~ 1892),IEEE,2020年12月,Canberra, Australia

  8. An Experimental Study for Tracking Ability of Deep Q-Network under the Multi-Objective Behaviour using a Mobile Robot with LiDAR

    Masashi Sugimoto, Ryunosuke Uchida, Shinji Tsuzuki, Hitoshi Sori, Hiroyuki Inoue, Kentaro Kurashige, Shiro Urushihara,Proceedings of 2020 International Conference on Big data, IoT, and Cloud Computing,2020年10月,Tokyo

  9. Proposal of Time-based evaluation for Universal Sensor Evaluation Index in Self-generation of Reward

    Afiqe Anuar bin Muhammad Nor Hakim, Koudai Fukuzawa, Kentaro Kurashige,Proceedings of 2020 IEEE International Conference on Systems, Man, and Cybernetics,(頁 1161 ~ 1166),IEEE,2020年10月,Toronto, Canada

  10. Robotized Counselor and Evaluation using Linguistic and Detection of Feeling and Polarity Change

    Kentarou Kurashige, Setsuo Tsuruta, Eriko Sakurai, Yoshitaka Sakurai, Rainer Knauf, Ernesto Damiani, Andrea Kutics,Proceedings of 2019 IEEE Symposium Series on Computational Intelligence,(頁 961 ~ 966),2019年12月,Xiamen, China

  11. Decision making on robot with multi-task using deep reinforcement learning for each task

    Yuya Shimoguchi, Kentarou Kurashige,Proceedings of 2019 IEEE International Conference on Systems, Man and Cybernetics,(頁 3440 ~ 3445),IEEE,2019年10月,Bari, Italy

  12. Counseling Robot Implementation and Evaluation

    Kentarou Kurashige, Setsuo Tsuruta, Eriko Sakurai, Yoshitaka Sakurai, Rainer Knauf, Ernesto Damiani, Andrea Kutics,Proceedings of 2018 IEEE International Conference on Systems, Man, and Cybernetics,(頁 1716 ~ 1722),IEEE,2018年12月,Miyazaki, Japan

  13. Self-Generation of Reward by Inputs from Multi Sensors -Integration of Evaluations for Inputs to Avoid Danger-

    Masaya Ishizuka, Kentaro Kurashige,2018 International Symposium on Micro-NanoMechatronics and Human Science,(頁 133 ~ 138),IEEE,2018年12月,Nagoya, Japan

  14. Action selection of robot by human intention estimated with dynamic evaluation criterion

    Yuya Shimoguchi, Seiya Shirakura, Kentarou Kurashige,Proceedings of IEEE Symposium Symposium Series on Computational Intelligence SSCI 2018,(頁 1785 ~ 1792),IEEE,2018年11月,BENGALURU, INDIA

  15. A study of dynamically adjustment for exploitation action using evaluation of achievement

    Masashi Sugimoto, Kentarou Kurashige,2017 International Symposium on Micro-NanoMechatronics and Human Science CD-ROM,(頁 352 ~ 356),IEEE,2017年12月,Nagoya

  16. Self-Generation of reward by human interaction -Adaptation to multmulti by reflecting hope degree for priority-

    Seiya Shirakura, Takuya Masaki, Masaya Ishizuka, Kentarou Kurashige,2017 International Symposium on Micro-NanoMechatronics and Human Science CD-ROM,(頁 283 ~ 288),IEEE,2017年12月,Nagoya

  17. Design of Counseling Robot for production by 3D printer

    Kentarou Kurashige, Setsuo Tsuruta, Eriko Sakurai, Yoshitaka Sakurai, Rainer Knauf, Ernesto Damiani,Proceedings of 13th International Conferenceon Signal-ImageTechnology and Internet-Based Systems,(頁 56 ~ 62),IEEE,2017年12月,Jaipur, India

  18. Context Respectful Counseling Agent integrated with Robot nodding for Dialog Promotion

    Kentarou Kurashige and Setsuo Tsuruta and Eriko Sakurai and Yoshitaka Sakurai and Rainer Knauf and Ernesto Damiani,Proc. of 2017 IEEE International Conference on Systems, Man, and Cybernetics,(頁 1540 ~ 1545),IEEE,2017年12月,Banff, Canada

  19. The Proposal of Self-Generation of Reward by the Universal Evaluations of Sensor Input from Human -Improvement of the Universal Evaluation with SVR-

    Masaya Ishizuka, Satoshi Ogata, Kentarou Kurashige,The 5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics,(頁 1 ~ 6),2017年11月,Beijing, P.R.China

  20. Action selection using priority for each task -Realization of effective processing by dynamical update of priority-

    Takuya Masaki, Kentarou Kurashige,Proceedings of 2017 6th International Conference on Informatics, Electronics and Vision & 2017 7th International Symposium in Computational Medical and Health Technology,IEEE,2017年09月,Himeji, Hyogo

  21. A Study for effectiveness of Dimensionality Reduction for State-action Pair Prediction -Training set reduction using Tendency-

    Masashi Sugimoto, Naoya Iwamoto, Robert Johnston, Keizo Kanazawa, Yukinori Misaki, Kentarou Kurashige,Proceedings of the Third International Conference on Electronics and Software Science (ICESS2017),(頁 19 ~ 28),2017年08月,Takamatsu, Japan

  22. Application and Performance Evaluation of a Lifting Device with Alternating Rotation Hoist

    Hanchao Li, Daisuke Harada, Naohiko Hanajima, Hidekazu Kajiwara, Kentaro Kurashige, Yoshinori Fujihira, Masato Mizukami,Proc. of 2016 IEEE/SICE International Symposium on System Integration,(頁 385 ~ 390),Article Number:, pp.385-390,December 13-15,2016,Sapporo,Japan,DOI:10.1109/SII.2016.7844029,ISSN:2474-2325,E-ISBN: 9,IEEE,2016年12月,Sapporo

  23. A STUDY OF EFFECTIVENESS OF DYNAMICALLY VARYING SAMPLING RATE FOR STATE-ACTION PAIR PREDICTION

    Masashi Sugimoto, Naoya Iwamoto, Robert W. Johnston, Keizo Kanazawa, Yukinori Misaki, Kentarou Kurashige,Proceedings of the International Conference on Electronics and Software Science,(頁 79 ~ 87),SDIWC,2016年11月,Takamatsu, Japan

  24. IoT-aware Context Respectful Counseling Agent

    Yukiko Yamamoto, Tetsuo Shinozaki, Setsuo Tsuruta, Kentarou Kurashige, Rainer Knauf,Proceedings of the IEEE International conference on systems,man,and cybernetics,(頁 4729 ~ 4736),IEEE,2016年10月,Budapest, Hungary

  25. Action Learning to Single Robot Using MARL with Repeated Consultation: Realization of Repeated Consultation Interruption for the Adaptation to Environmental Change

    Yoh TAKADA, Kentarou KURASHIGE,Intelligent Robotics and Applications, Proceedings, Part II,(頁 371 ~ 382),Springer International Publishing,2016年08月,Tokyo, Japan

  26. Action selection using each task priority:Realization of asynchrony action selection and update priorities

    Takuya Masaki, Kentarou Kurashige,2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems,(頁 908 ~ 911),IEEE,2016年08月,Sapporo, Hokkaido

  27. Effective action selection under multi task by ignoring tasks and limiting tasks

    Takuya Masaki, Kentarou Kurashige,Proceedings of World Automation Congress 2016,(頁 1 ~ 6),Article Number:1570248406,2016年08月,Puerto Rico

  28. Nodding Behavioral Context Respectful Counseling Agent

    Yukiko Yamamoto, Tetsuo Shinozaki, Setsuo Tsuruta, Kentarou Kurashige, Rainer Knauf,Proceedings of World Automation Congress 2016,(頁 1 ~ 6),Article Number:1570277693,2016年08月,Puerto Rico

  29. A Study on the Deciding an Action Based on the Future Probabilistic Distribution

    Masashi SUGIMOTO, Kentarou KURASHIGE,Intelligent Robotics and Applications, Proceedings, Part II,(頁 383 ~ 394),Springer International Publishing,2016年08月,Tokyo, Japan

  30. The Proposal for Compensation to the Action of Motion Control based on the Prediction of State-action Pair

    Masashi Sugimoto, Kentarou Kurashige,Proceedings of the International Conference on Electronics and SoftwareScience,(頁 35 ~ 84),CD-ROM,2015年07月,Takamatsu, Japan

  31. Action Learning to single robot using MAS -A proposal of Agents action decision method based repeated consultation-

    Shuhei Chiba, Kentarou Kurashige,Proceedings of the 10th Asian Control Conference,IEEE,2015年05月,Kota Kinabalu, Malaysia

  32. Teaching a series of actions by the universal evaluations of each sensory information

    Kentarou Kurashige, Kaoru Nikaido,2015 IEEE Congress on Evolutionary Computation,(頁 2341 ~ 2346),IEEE,2015年05月,Sendai, Japan

  33. The Proposal for Real-time Sequential-decision for Optimal Action using Flexible-weight Coefficient based on the State-Action Pair

    Masashi Sugimoto, Kentarou Kurashige,2015 IEEE Congress on Evolutionary Computation,(頁 544 ~ 551),IEEE,2015年05月,Sendai, Japan

  34. Self-generation of reward in reinforcement learning by universal rules of interaction with the external environment'

    Kentarou Kurashige, Kaoru Nikaido,2014 IEEE Symposium on Robotic Intelligence in Informationally Structured Space Proceedings,(頁 92 ~ 97),IEEE,2014年12月,Orlando, Florida, U.S.A.

  35. Real-time Sequentially Decision for Optimal Action using Prediction of the State-Action Pair

    Masashi Sugimoto, Kentarou Kurashige,Proceedings of 2014 International Symposium on Micro-NanoMechatronics and Human Science,(頁 199 ~ 204),IEEE,2014年11月,Nagoya, Japan

  36. Self-Generation of reward by moderate-based index in reinforcement learning

    Kentarou Kurashige, Kaoru Nikaido,The International Workshop on Advanced Computational Intelligence and Intelligent Informatics 2014(IWACIII2014),(頁 IWACIII2014-03 ~ ),2014年02月,Fukui

  37. Control of exploration and exploitation using information content'

    Nodoka Shibuya, Kentarou Kurashige,The Nineteenth International Symposium on Artificial Life and Robotics 2014 (AROB 19th 2014),(頁 48 ~ 51),ISAROB,2014年01月,Beppu

  38. Self-Generation of Reward by Sensor Input in Reinforcement Learning

    Kaoru Nikaido, Kentarou Kurashige,2013 Second International Conference on Robot, Vision and Signal Processing,(頁 270 ~ 273),IEEE,2013年12月,Kitakyushu

  39. The Proposalfor Deciding Effective Action using Prediction of Internal Robot State Based on Internal State and Action

    Masashi Sugimoto, Kentarou Kurashige,Proceedings of 2013 International Symposium on Micro-NanoMechatronics and Human Science,(頁 221 ~ 226),2013年11月,Nagoya

  40. The Proposal for Prediction of Internal Robot State Based on Internal State and Action

    Masashi Sugimoto, Kentarou Kurashige,Proc. of IWACIII2013 CD-ROM,(頁 SS1-2 ~ ),CD-ROM,2013年10月,Shanghai

  41. Proposal of learning method which selects objectives based on the state

    Hironori Miura, Kentarou Kurashige,Proceedings of 2013 IEEE Workshop on Robotic Intelligence in Informationally Structured Space at 2013 IEEE Symposium Series on Computational Intelligence(RiiSS at SSCI),(頁 119 ~ 124),CD-ROM,2013年04月,Singapore

  42. Reduction of state space on reinforcement learning by sensor selection

    Yasutaka Kishima, Kentarou Kurashige,Proceedings of 2012 International Symposium on Micro-NanoMechatronics and Human Science(MSH2012&Micro-Nano Global COE),(頁 138 ~ 143),2012年11月,Nagoya, Japan

  43. Estimate of currentstate based on experience in POMDP for Reinforcement Learning

    Yoshiki Miyazaki, Kentarou Kurashige,Proceedings of the seventeenth International Symposium on Artificial Life and Robotics (AROB 17th '12),(頁 1135 ~ 1138),2012年01月,Beppu, Japan

  44. Reduction of learningspace by making a choice of sensor information

    Yasutaka Kishima, Kentarou Kurashige, Toshinobu Numata,Proceedings of the seventeenth International Symposium on Artificial Life and Robotics (AROB 17th '12),(頁 971 ~ 974),2012年01月,Beppu, Japan

  45. Proposal of method "Motion Space" to express movement of the robot

    Naoki Kitayama, Kentarou Kurashige,Proc. of IWACIII2011 CD-ROM,-巻,-号,(頁 GS1-3 ~ ),CD-ROM,2011年11月,Suzhou, China

  46. Suggestion of Probabilistic Reward-Independent Knowledge for Dynamic Environment in Reinforcement Learning

    Nodoka Shibuya, Yoshiki Miyazaki, Kentarou Kurashige,2011 Int. Symp. on Micro-NanoMechatronics and Human Science CD-ROM,-巻,-号,(頁 140 ~ 145),CD-ROM,2011年11月,Nagoya, Japan

  47. Use of reward - independent knowledge on reinforcementlearning for dynamic environment

    Yoshiki Miyazaki and Kentarou Kurashige,Proc. of the International Conference on Advanced Computer Science and Information Systems(ICACSIS 2010) pp.303-309, Bali, 20th - 23rd november2010,(頁 303 ~ 309),2010年11月,Bali, Indonesia

  48. THE GROWTH OF INDIVIDUAL INTELLIGENCE IN GROUPS OF AGENTS BYAUTONOMOUS SELECTION OF OTHERS TO COMMUNICATE TO

    Yasutaka Kishima, Kentarou Kurashige,WAC 2010,-巻,-号,(頁 IFMIP-545 ~ ),CD-ROM,2010年09月,Kobe, Japan

  49. Use of the knowledge which is independence on reward in Reinforcement Learning

    Yoshiki Miyazaki, Kentarou Kurashige,Proc. of International Symposium on Computational Intelligence in Robotics and Automation (CD-ROM),(頁 114 ~ 119),CD-ROM,2009年12月,Daejeon, Korea

  50. Growth ofindividual intelligence using communication

    Yasutaka Kishima, Kentarou Kurashige,SCIS&ISIS 2008(CD-ROM),-巻,-号,(頁 287 ~ 292),CD-ROM,2008年09月,Nagoya

  51. A RELATIONSHIP BETWEEN ABILITY OF PERCEPTION AND LEARNING EFFICIENCY

    Yukiko Onoue,Kentarou Kurashige,WAC 2008 Congress CD-ROM,-巻,-号,(頁 IFMIP-521 ~ ),CD-ROM,2008年09月,Hawaii

  52. The robot learning by using

    Kentarou Kurashige, Yukiko Onoue,Proceedings of International Symposium on Humanized Systems 2007,(頁 1 ~ 4),CD-ROM,2007年09月,Muroran

  53. A simple rule how to make a reward for learning with human interaction.

    Kentarou Kurashige,Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation,(頁 202 ~ 205),2007年,Jacksonville, FLORIDA

  54. Control program structure of Humanoid Robot

    Shigeto Aramaki, Hiroshi Shirouzu, Kentarou Kurashige, Taketoshi Kinoshita,Proc.of Int. Conference of IEEE Industrial Electronics Society,-巻,-号,(頁 1796 ~ 1800),-,2002年11月,-

  55. Motion planning based on hierarchical knowledge for six legged locomotion robot

    K. Kurashige, T. Fukuda, H. Hoshino,Proc. of Int. Conf. on Systems, Man, and Cybernetics,IV巻,-号,(頁 924 ~ 929),-,1999年10月,Tokyo

  56. Motion planning based on hierarchical knowledge using Genetic Programming

    K. Kurashige,T. Fukuda, H. Hoshino,Proc. of Int. Conf. on Robotics and Automation,-巻,-号,(頁 2464 ~ 2469),-,1999年05月,Detroit

このページの先頭へ▲