Updated on 2021/04/22

写真a

 
UWANO Fumito
 
Organization
Natural Science and Technology Assistant Professor
Position
Assistant Professor
Contact information
メールアドレス
Profile
2017年4月~2020年3月 電気通信大学 博士後期課程 学生(日本学術振興会特別研究員DC1)

研究内容:静的・動的環境における通信なしマルチエージェント強化学習
本研究は,現実問題に起こる通信遅延や情報の不確かさに対処するために,複数のエージェント間の協調行動を通信なしで導く強化学習手法を提案するとともに,変化のない静的環境に加えて不測の事態などで変化する動的環境に対応できるように拡張した.迷路問題にて比較実験を行った結果,静的環境のみならず動的環境においても,提案手法は従来のQ学習よりも多くの報酬を早く獲得可能であることを明らかにした.

2020年4月~現在 岡山大学 助教

研究内容1:抽象度の異なる協調行動を学習可能なエージェントの提案
本研究では,ロボットのように周囲の環境から得た情報を基に行動を決める主体(エージェント)が,複数集まったときの適切な行動則を獲得するマルチエージェント強化学習を実用化する上での,センサの個体差や状況の違いによる,観測情報の粒度の違いに適応した協調行動学習法を提案する.具体的には,エージェントにおける情報の抽象度を制御し,獲得情報の粒度に従ってエージェント毎の抽象度を調整することで,適切な協調行動を学習する.

研究内容2:未知の協調・環境を想定したマルチエージェント強化学習の知識転移
本研究では,ロボットなどの活動主体(エージェント)が複数存在するときの協調制御ルールを,周囲環境から得た情報から各々が学習するマルチエージェント強化学習において,学習すべき協調や環境が未知であるときに適応した協調行動学習法を提案する.具体的には,他の環境などで今まで学習したエージェントの学習結果を各要素に分割し,階層的に抽象化することで生成した知識を組み合わせて学習することで未知の協調・環境に適応する.

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Degree

  • Doctor (Engineering) ( 2020.3   The University of Electro-Communications )

Research Interests

  • Human-Agent Interaction

  • Space Engineering

  • Evolutionary Machine Learning

  • Social Simulation

  • Healthcare Informatics

  • Evolutionary Computation

  • Multi-agent System

  • Reinforcement Learning

Research Areas

  • Informatics / Theory of informatics

  • Informatics / Information network

  • Informatics / Intelligent robotics

  • Informatics / Intelligent informatics

Education

  • The University of Electro-Communications   情報理工学研究科   情報学専攻

    2017.4 - 2020.3

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  • The University of Electro-Communications   情報理工学研究科   総合情報学専攻

    2015.4 - 2017.3

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  • The University of Electro-Communications   情報理工学部   総合情報学科

    2011.4 - 2015.3

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Research History

  • Okayama University   Academic Research Institute of Natural Science and Technology   Assistant Professor

    2021.4

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    Country:Japan

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  • Okayama University   The Graduate School of Natural Science and Technology   Assistant Professor

    2020.4 - 2021.3

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    Country:Japan

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  • The University of Electro-Communications     Part-time staff

    2020.1 - 2020.2

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  • Japan Society for the Promotion of Science     Research Fellowship for Young Scientists (DC1)

    2017.4 - 2020.3

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Professional Memberships

  • The Robotics Society of Japan

    2020.8

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  • The Japan Society for Aeronautical and Space Sciences

    2020.7

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  • Japan Association of Simulation and Gaming

    2020.7

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  • Institute of Electronics, Information and Communication Engineers (IEICE)

    2020.5

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  • THE INSTITUTE OF ELECTRICAL ENGINEERS OF JAPAN

    2019.9

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  • The Japanese Society for Evolutionary Computation

    2019.9

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  • Association for Computing Machinery (ACM)

    2019.4

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  • Institute of Electrical and Electronics Engineers (IEEE)

    2019.4

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  • INFORMATION PROCESSING SOCIETY OF JAPAN

    2018.8

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  • THE JAPANESE SOCIETY FOR ARTIFICIAL INTELLIGENCE

    2018.7

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  • THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS

    2018.6

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  • UNISEC

    2014.4 - 2018.3

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Papers

  • How to Emote for Consensus Building in Virtual Communication Reviewed

    Yoshimiki Maekawa, Fumito Uwano, Eiki Kitajima, Keiki Takadama

    Proceedings of 22nd International Conference on Human-Computer Interaction   2020.7

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  • Reward Value-Based Goal Selection for Agents’ Cooperative Route Learning Without Communication in Reward and Goal Dynamism Reviewed

    Fumito Uwano, Keiki Takadama

    SN Computer Science1 ( 3 )   2020.5

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    DOI: 10.1007/s42979-020-00191-2

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    Other Link: http://link.springer.com/article/10.1007/s42979-020-00191-2/fulltext.html

  • Directionality Reinforcement Learning to Operate Multi-Agent System without Communication Reviewed

    Fumito Uwano, Keiki Takadama

    Proceedings of 11th International Workshop on Optimization and Learning in Multiagent Systems (OptLearnMAS2020)   2020.5

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  • How to Design Adaptable Agents to Obtain a Consensus with Omoiyari. Reviewed

    Yoshimiki Maekawa, Fumito Uwano, Eiki Kitajima, Keiki Takadama

    Human Interface and the Management of Information. Visual Information and Knowledge Management - Thematic Area, HIMI 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26-31, 2019, Proceedings, Part I   462 - 475   2019.7

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    Publisher:Springer  

    DOI: 10.1007/978-3-030-22660-2_34

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  • How to Select Appropriate Craters to Estimate Location Accurately in Comprehensive Situations for SLIM Project Reviewed

    Fumito Uwano, Takato Tatsumi, Akinori Murata, Keiki Takadama, Hiroyuki Kamata, Takayuki Ishida, Seisuke Fukuda, Shujiro Sawai, Shinichiro Sakai

    Proceedings of the 32nd International Symposium on Space Technology and Science (ISTS) & 9th Nano-Satellite Symposium (NSAT)   2019.6

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  • Bat Algorithm with Dynamic Niche Radius for Multimodal Optimization Reviewed

    Takuya Iwase, Ryo Takano, Fumito Uwano, Hiroyuki Sato, Keiki Takadama

    Proceedings of the 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI 2019)   2019.3

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  • Maximum Entropy Inverse Reinforcement Learning with Incomplete Experts Reviewed

    Satoshi Hasegawa, Fumito Uwano, Keiki Takadama

    Proceedings of the 24th International Symposium on Artificial Life and Robotics   2019.1

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  • Utilizing Observed Information for No-Communication Multi-agent Reinforcement Learning toward Cooperation in Dynamic Environment Reviewed

    Fumito Uwano, Keiki Takadama

    SICE Journal of Control, Measurement, and System Integration   2019

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  • 目的制限に基づく通信なしマルチエージェント協調行動学習とその効果の証明 Reviewed

    Fumito Uwano, Keiki Takadama

    電気学会 論文誌C140 ( 1 )   2019

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  • Niche Radius Adaptation in Bat Algorithm for Locating Multiple Optima in Multimodal Functions. Reviewed

    Takuya Iwase, Ryo Takano, Fumito Uwano, Hiroyuki Sato, Keiki Takadama

    IEEE Congress on Evolutionary Computation, CEC 2019, Wellington, New Zealand, June 10-13, 2019   800 - 807   2019

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    Publisher:IEEE  

    DOI: 10.1109/CEC.2019.8790087

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  • Novelty Search-based Bat Algorithm: Adjusting Distance among Solutions for Multimodal Optimization Reviewed

    Takuya Iwase, Ryo Takano, Fumito Uwano, Hiroyuki Sato, Keiki Takadama

    Proceedings of the 22nd Asia Pacific Symposium on Intelligent and Evolutionary Systems   2018.12

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  • Strategy for Learning Cooperative Behavior with Local Information for Multi-agent Systems Reviewed

    Fumito Uwano, Keiki Takadama

    Proceedings of The 21st International Conference on Principles and Practice of Multi-Agent Systems   663 - 670   2018.10

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    DOI: 10.1007/978-3-030-03098-8_54

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  • Multiple swarm intelligence methods based on multiple population with sharing best solution for drastic environmental change. Reviewed

    Yuta Umenai, Fumito Uwano, Hiroyuki Sato, Keiki Takadama

    Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, July 15-19, 2018   97 - 98   2018.7

  • Weighted Opinion Sharing Model for Cutting Link and Changing Information among Agents as Dynamic Environment Reviewed

    Fumito Uwano, Rei Saito, Keiki Takadama

    SICE Journal of Control, Measurement, and System Integration11 ( 4 ) 331 - 340   2018.7

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    DOI: 10.9746/jcmsi.11.331

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  • Multi-Agent Cooperation Based on Reinforcement Learning with Internal Reward in Maze Problem Reviewed

    Fumito Uwano, Naoki Tatebe, Yusuke Tajima, Masaya Nakata, Tim Kovacs, Keiki Takadama

    SICE Journal of Control, Measurement, and System Integration11 ( 4 ) 321 - 330   2018.7

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    DOI: 10.9746/jcmsi.11.321

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  • Correcting Wrongly Determined Opinions of Agents in Opinion Sharing Model. Reviewed

    Eiki Kitajima, Caili Zhang, Haruyuki Ishii, Fumito Uwano, Keiki Takadama

    Human Interface and the Management of Information. Interaction, Visualization, and Analytics - 20th International Conference, HIMI 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, July 15-20, 2018, Proceedings, Part I   658 - 676   2018.7

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    Publisher:Springer  

    DOI: 10.1007/978-3-319-92043-6_52

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  • Generalizing rules by random forest-based learning classifier systems for high-dimensional data mining. Reviewed

    Fumito Uwano, Koji Dobashi, Keiki Takadama, Tim Kovacs

    Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, July 15-19, 2018   1465 - 1472   2018.7

  • How to Detect Essential Craters in Camera Shot Image to Increase the Number of Spacecraft Location Estimation while Improving its Accuracy? Reviewed

    Haruyuki Ishii, Yuta Umenai, Kazuma Matsumoto, Fumito Uwano, Takato Tatsumi, Keiki Takadama, Hiroyuki Kamata, Takayuki Ishida, Seisuke Fukuda, Shujiro Sawai, Shinichiro Sakai

    Proceedings of The International Symposium on Artificial Intelligence, Robotics and Automation in Space, i-SAIRAS 2018   2018.6

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  • Analyzing Triangle Matching Method Based on Craters for Spacecraft Localization Reviewed

    Fumito Uwano, Haruyuki Ishii, Yuta Umenai, Kazuma Matsumoto, Takato Tatsumi, Akinori Murata, Keiki Takadama

    Proceedings of The International Symposium on Artificial Intelligence, Robotics and Automation in Space, i-SAIRAS 2018   2018.6

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  • Ensemble Heart Rate Extraction Method for Biological Data from Water Pressure Sensor Reviewed

    Fumito Uwano, Keiki Takadama

    Proceedings of 2018 AAAI Spring Symposium Series   304 - 309   2018.3

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  • Improving Sleep Stage Estimation Accuracy by Circadian Rhythm Extracted from a Low Frequency Component of Heart Rate

    Akari Tobaru, Fumito Uwano, Takuya Iwase, Kazuma Matsumoto, Ryo Takano, Yusuke Tajima, Yuta Umenai, Keiki Takadama

    Proceedings of 2018 AAAI Spring Symposium Series   297 - 303   2018.3

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  • Strategy for Learning Cooperative Behavior with Local Information for Multi-agent Systems. Reviewed

    Fumito Uwano, Keiki Takadama

    PRIMA 2018: Principles and Practice of Multi-Agent Systems - 21st International Conference, Tokyo, Japan, October 29 - November 2, 2018, Proceedings   663 - 670   2018

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    Publisher:Springer  

    DOI: 10.1007/978-3-030-03098-8_54

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  • Sleep Stage Estimation Comparing Own Past Heartrate or Others' Heartrate

    TAJIMA Yusuke, UWANO Fumito, MURATA Akinori, HARADA Tomohiro, TAKADAMA Keiki

    SICE Journal of Control, Measurement, and System Integration11 ( 1 ) 32‐39(J‐STAGE)   2018

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  • SLIM Spacecraft Location Estimation by Crater Matching Based on Similar Triangles and Its Improvement

    ISHII Haruyuki, MURATA Akinori, UWANO Fumito, TATSUMI Takato, UMENAI Yuta, TAKADAMA Keiki, HARADA Tomohiro, KAMATA Hiroyuki, ISHIDA Takayuki, FUKUDA Seisuke, SAWAI Shujiro, SAKAI Shinichiro

    AEROSPACE TECHNOLOGY JAPAN, THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES17 ( 0 ) 69 - 78   2018

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    Language:Japanese   Publisher:一般社団法人 日本航空宇宙学会  

    This paper focuses on the Evolutional Triangle Similarity Matching (ETSM) method for estimating spacecraft location in Smart Lander for Investigating Moon (SLIM) mission and improves it by adding the functions of elimination of line symmetric triangles between crater map and camera shot image, comparison of rotation relationship of triangles and triangle formation method using Delaunay triangulation and introducing point group matching as a coordinate calculation function. To evaluate the robustness of the improved method, we conduct simulation experiments using the crater map and camera shot images in six situations. This experiments have revealed the following implications: (1) this method improved accuracy of location estimation within 5.1 pixels by the functions of elimination of line symmetric triangles between crater map and camera shot image, (2) this method slight got worse accuracy at low or high altitude of spacecraft, however, this method successfully reduced incorrect spacecraft location estimation by comparison of rotation relationship of triangles, (3) this method improved accuracy of location estimation by triangle formation method using Delaunay triangulation, but possibility of incorrect spacecraft location estimation is slight increased, and (4) integration method of these three mechanism can estimate spacecraft location within 5 pixels without being affected altitude difference and rotation of camera shot image.

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  • Ensemble Heart Rate Extraction Method for Biological Data from Water Pressure Sensor. Reviewed

    Fumito Uwano, Keiki Takadama

    2018 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 26-28, 2018.   2018

  • Improving Sleep Stage Estimation Accuracy by Circadian Rhythm Extracted from a Low Frequency Component of Heart Rate. Reviewed

    Akari Tobaru, Fumito Uwano, Takuya Iwase, Kazuma Matsumoto, Ryo Takano, Yusuke Tajima, Yuta Umenai, Keiki Takadama

    2018 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 26-28, 2018.   2018

  • Recovery system based on exploration-biased genetic algorithm for stuck rover in planetary exploration Reviewed

    Fumito Uwano, Yusuke Tajima, Akinori Murata, Keiki Takadama

    Journal of Robotics and Mechatronics29 ( 5 ) 877 - 886   2017.10

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    Contributing toward continuous planetary surface exploration by a rover (i.e., a space probe), this paper proposes (1) an adaptive learning mechanism as the software system, based on an exploration-biased genetic algorithm (EGA), which intends to explore several behaviors, and (2) a recovery system as the hardware system, which helps a rover exit stuck areas, a kind of immobilized situation, by testing the explored behaviors. We develop a rover-type space probe, which has a stabilizer with two movable joints like an arm, and learns how to use them by employing EGA. To evaluate the effectiveness of the recovery system based on the EGA, the following two field experiments are conducted with the proposed rover: (i) a small field test, including a stuck area created artificially in a park
    and (ii) a large field test, including several stuck areas in Black Rock Desert, USA, as an analog experiment for planetary exploration. The experimental results reveal the following implications: (1) the recovery system based on the EGA enables our rover to exit stuck areas by an appropriate sequence of motions of the two movable joints
    and (2) the success rate of getting out of stuck areas is 95% during planetary exploration.

    DOI: 10.20965/jrm.2017.p0877

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  • Supporting the exploration of the learning goals for a continuous learner toward creative learning Reviewed

    Takato Okudo, Tomohiro Yamaguchi, Akinori Murata, Takato Tatsumi, Fumito Uwano, Keiki Takadama

    Journal of Advanced Computational Intelligence and Intelligent Informatics21 ( 5 ) 907 - 916   2017.9

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    This paper proposes a learning goal space that visualizes the distribution of the obtained solutions to support the exploration of the learning goals for a learner. Subsequently, we examine the method for assisting a learner to present the novelty of the obtained solution. We conduct a learning experiment using a continuous learning task to identify various solutions. To assign the subjects space to explore the learning goals, several parameters related to the success of the task are not instructed to the subjects. In the comparative experiment, three types of learning feedbacks provided to the subjects are compared. These are presenting the learning goal space with obtained solutions mapped on it, directly presenting the novelty of the obtained solutions mapped on it, and presenting some value that is slightly related to the obtained solution. In the experiments, the subjects to whom the learning goal space or novelty of the obtained solution is shown, continue to identify solutions according to their learning goals until the final stage in the experiment is attained. Therefore, in a continuous learning task, our supporting method of directly or indirectly presenting the novelty of the obtained solution through the learning goal space is effective.

    DOI: 10.20965/jaciii.2017.p0907

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  • Comparison between reinforcement learning methods with different goal selections in multi-agent cooperation Reviewed

    Fumito Uwano, Keiki Takadama

    Journal of Advanced Computational Intelligence and Intelligent Informatics21 ( 5 ) 917 - 929   2017.9

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Fuji Technology Press  

    This study discusses important factors for zero communication, multi-agent cooperation by comparing different modified reinforcement learning methods. The two learning methods used for comparison were assigned different goal selections for multi-agent cooperation tasks. The first method is called Profit Minimizing Reinforcement Learning (PMRL)
    it forces agents to learn how to reach the farthest goal, and then the agent closest to the goal is directed to the goal. The second method is called Yielding Action Reinforcement Learning (YARL)
    it forces agents to learn through a Q-learning process, and if the agents have a conflict, the agent that is closest to the goal learns to reach the next closest goal. To compare the two methods, we designed experiments by adjusting the following maze factors: (1) the location of the start point and goal
    (2) the number of agents
    and (3) the size of maze. The intensive simulations performed on the maze problem for the agent cooperation task revealed that the two methods successfully enabled the agents to exhibit cooperative behavior, even if the size of the maze and the number of agents change. The PMRL mechanism always enables the agents to learn cooperative behavior, whereas the YARL mechanism makes the agents learn cooperative behavior over a small number of learning iterations. In zero communication, multi-agent cooperation, it is important that only agents that have a conflict cooperate with each other.

    DOI: 10.20965/jaciii.2017.p0917

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  • The robust spacecraft location estimation algorithm toward the misdetection crater and the undetected crater in SLIM Reviewed

    Haruyuki Ishii, Keiki Takadama, Akinori Murata, Fumito Uwano, Takato Tatsumi, Yuta Umenai, Kazuma Matsumoto, Hiroyuki Kamata, Takayuki Ishida, Seisuke Fukuda, Shujiro Sawai, Shinichiro Sakai

    Proceedings of International Symposium on Space Technology and Science, ISTS 2017   2017.6

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  • Strategies to improve cuckoo search toward adapting randomly changing environment Reviewed

    Yuta Umenai, Fumito Uwano, Hiroyuki Sato, Keiki Takadama

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)10385   573 - 582   2017

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:Springer Verlag  

    Cuckoo Search (CS) is the powerful optimization algorithm and has been researched recently. Cuckoo Search for Dynamic Environment (D-CS) has proposed and tested in dynamic environment with multi-modality and cyclically before. It was clear that has the hold capability and can find the optimal solutions in this environment. Although these experiments only provide the valuable results in this environment, D-CS not fully explored in dynamic environment with other dynamism. We investigate and discuss the find and hold capabilities of D-CS on dynamic environment with randomness. We employed the multi-modal dynamic function with randomness and applied D-CS into this environment. We compared D-CS with CS in terms of getting the better fitness. The experimental result shows the D-CS has the good hold capability on dynamic environment with randomness. Introducing the Local Solution Comparison strategy and Concurrent Solution Generating strategy help to get the hold and find capabilities on dynamic environment with randomness.

    DOI: 10.1007/978-3-319-61824-1_62

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  • Communication-Less Cooperative Q-Learning Agents in Maze Problem Reviewed

    Fumito Uwano, Keiki Takadama

    INTELLIGENT AND EVOLUTIONARY SYSTEMS, IES 20168   453 - 467   2017

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:SPRINGER INT PUBLISHING AG  

    This paper introduces a reinforcement learning technique with an internal reward for a multi-agent cooperation task. The proposed method is an extension of Q-learning which changes the ordinary (external) reward to the internal reward for agent-cooperation under the condition of no communication. To increase the certainty of the proposed methods, we theoretically investigate what values should be set to select the goal for the cooperation among agents. In order to show the effectiveness of the proposed method, we conduct the intensive simulation on the maze problem for the agent-cooperation task, and confirm the following implications: (1) the proposed method successfully enable agents to acquire cooperative behaviors while a conventional method fails to always acquire such behaviors; (2) the cooperation among agents according to their internal rewards is achieved no communication; and (3) the condition for the cooperation among any number of agent is indicated.

    DOI: 10.1007/978-3-319-49049-6_33

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  • Adaptive Learning Based on Genetic Algorithm for Rover in Planetary Exploration Reviewed

    Fumito Uwano, Akinori Murata, Keiki Takadama

    Proceedings of The International Symposium on Artificial Intelligence, Robotics and Automation in Space, i-SAIRAS 2016   2016.6

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  • Reinforcement learning with internal reward for multi-Agent cooperation: A theoretical approach Reviewed

    Fumito Uwano, Naoki Tatebe, Masaya Nakata, Keiki Takadama, Tim Kovacs

    BICT 2015 - 9th EAI International Conference on Bio-Inspired Information and Communications Technologies2 ( 8 ) e2   2016.5

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:Association for Computing Machinery, Inc  

    This paper focuses on a multi-Agent cooperation which is generally difficult to be achieved without sufficient information of other agents, and proposes the reinforcement learning method that introduces an internal reward for a multi-Agent cooperation without sufficient information. To guarantee to achieve such a cooperation, this paper theoretically derives the condition of selecting appropriate actions by changing internal rewards given to the agents, and extends the reinforcement learning methods (Q-learning and Profit Sharing) to enable the agents to acquire the appropriate Q-values up- dated according to the derived condition. Concretely, the internal rewards change when the agents can only find better solution than the current one. The intensive simulations on the maze problems as one of test beds have revealed the following implications:(1) our proposed method successfully enables the agents to select their own appropriate cooperating actions which contribute to acquiring the minimum steps towards to their goals, while the conventional methods (i.e., Q-learning and Profit Sharing) cannot always acquire the minimum steps
    and (2) the proposed method based on Profit Sharing provides the same good performance as the proposed method based on Q-learning.

    DOI: 10.4108/eai.3-12-2015.2262878

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  • Real-Time Sleep Stage Estimation from Biological Data with Trigonometric Function Regression Model. Reviewed

    Tomohiro Harada, Fumito Uwano, Takahiro Komine, Yusuke Tajima, Takahiro Kawashima, Morito Morishima, Keiki Takadama

    2016 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 21-23, 2016   2016

  • A Modified Cuckoo Search Algorithm for Dynamic Optimization Problems Reviewed

    Yuta Umenai, Fumito Uwano, Yusuke Tajima, Masaya Nakata, Hiroyuki Sato, Keiki Takadama

    2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)   1757 - 1764   2016

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    This paper proposes a simple modification of the Cuckoo Search called CS for a dynamic environment. In this paper, we consider a dynamic optimization problem where the global optimum can be cyclically changed depending on time. Our modified CS algorithm holds good candidates in order to effectively explore the search space near those candidates with an intensive local search. Our first experiment tests the prosed method on a set of static optimization problems, which aims at evaluating the potential performance of the proposed method. Then, we apply it to a dynamic optimization problem. Experimental results on the static problems show that the proposed method derives a better performance than the conventional method, which suggest the proposed method potentially has a good capability of finding a good solution. On the dynamic problem, the proposed method also performs well while the conventional method fails to find a better solution.

    DOI: 10.1109/CEC.2016.7744001

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  • Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach. Reviewed

    Fumito Uwano, Naoki Tatebe, Masaya Nakata, Keiki Takadama, Tim Kovacs

    BICT 2015, Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS), New York City, United States, December 3-5, 2015   332 - 339   2015

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MISC

  • BERTを利用した煽りツイート検出の一手法

    Norihisa Matsumoto, Fumito Uwano, Manabu Ohta

        2021.3

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    Language:Japanese   Publishing type:Research paper, summary (national, other academic conference)  

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  • 引用意図を利用した学術論文閲覧支援情報生成の一手法

    Masayoshi Nishiumi, Teruhito Kanazawa, Atsuhiro Takasu, Fumito Uwano, Manabu Ohta

    第13回データ工学と情報マネジメントに関するフォーラム講演論文集   2021.3

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  • Twitterを利用した旅行者の状況推定と観光ルート推薦

    Chihiro Takeshita, Fumito Uwano, Manabu Ohta

    第13回データ工学と情報マネジメントに関するフォーラム講演論文集   2021.3

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  • Flickrとじゃらんnetを利用した穴場スポットの発見手法

    Hikaru Nomoto, Fumito Uwano, Manabu Ohta

    第13回データ工学と情報マネジメントに関するフォーラム講演論文集   2021.3

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  • 文のマルチカテゴリ分散表現の獲得とその応用

    Kentaro Tani, Fumito Uwano, Manabu Ohta

    第13回データ工学と情報マネジメントに関するフォーラム講演論文集   2021.3

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  • ニューラルネットワークを用いた表構造解析の一手法

    Hiroyuki Aoyagi, Teruhito Kanazawa, Atsuhiro Takasu, Fumito Uwano, Manabu Ohta

    第13回データ工学と情報マネジメントに関するフォーラム講演論文集   2021.3

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  • 評判情報の特徴軸を考慮した可視化システム

    Apollon Nishikawa, Fumito Uwano, Manabu Ohta

        2021.3

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  • マルチエージェントシステムにおける協調行動の抽象度と深層強化学習器の関係性の考察

    Fumito Uwano, Mitsuki Sakamoto

    第48回知能システムシンポジウム講演論文集   2021.3

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  • ユーザの興味を利用した学術論文閲覧支援の一手法

    Takumi Iwamoto, Teruhito Kanazawa, Fumito Uwano, Manabu Ohta

        2021.3

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  • BERTによる参考文献書誌情報抽出精度の向上

    Ryohei Arakawa, Teruhito Kanazawa, Atsuhiro Takasu, Fumito Uwano, Manabu Ohta

        2021.3

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  • COVID-19の感染症対策を考慮した観光ルート推薦の一手法

    Shuto Konami, Fumito Uwano, Manabu Ohta

    2021年電子情報通信学会総合大会情報・システムソサイエティ特別企画ジュニア&学生ポスターセッション予稿集   2021.3

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  • Distributed Representation of Sentence with Attributes of Items Based on Rakuten Review Data

    Kentaro Tani, Fumito Uwano, Manabu Ohta

    NII-IDR User Forum 2020   2020.11

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  • マルチエージェント強化学習による目的数の異なるエージェント間の目的推定

    Mitsuki Sakamoto, Yoshimiki Maekawa, Eiki Kitajima, Fumito Uwano, Keiki Takadama

    第47回知能システムシンポジウム講演論文集   2020.3

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  • 逆強化学習における準最適行動系列からの最適行動獲得に向けたエキスパート行動の修正

    Satoshi Hasegawa, Fumito Uwano, Keiki Takadama

    Proceedings of SICE SSI 2019   2019.11

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  • クレータの座標ずれを利用したSLIM探査機の自己位置推定精度の向上

    Yuka Waragai, Fumito Uwano, Keiki Takadama

    第63回宇宙科学技術連合講演会講演論文集   2019.11

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  • 多次元意見共有エージェントネットワークモデルにおける複数の環境情報発信源を考慮した誤報伝搬防止アルゴリズム

    Eiki Kitajima, Akinori Murata, Fumito Uwano, Keiki Takadama

    Proceedings of SICE SSI 2019   2019.11

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  • 非通信マルチエージェント強化学習における獲得報酬値の変動を用いたエージェント数の動的変化への追従

    Fumito Uwano, Keiki Takadama

    第18回情報科学技術フォーラム講演資料集   2019.9

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  • エージェント間通信を伴わず環境状態および報酬の包括的動的変化に追従する理論的マルチエージェント強化学習 Reviewed

    Fumito Uwano, Keiki Takadama

    合同エージェントワークショップ&シンポジウム2019講演資料集   2019.9

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  • 集団適応を導くギャップ補填に基づく「思いやり」

    Yoshimiki Maekawa, Fumito Uwano, Eiki Kitajima, Keiki Takadama

    第33回人工知能学会全国大会   2019.6

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  • 故障に対して冗長性を備えた仮想ロボットのニューロ進化による持続可能な行動獲得

    速水陽平, 辰巳嵩豊, 上野史, 高玉圭樹

    知能システムシンポジウム講演資料(CD-ROM)46th   ROMBUNNO.B4‐3   2019.3

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  • 好奇心を持つエージェントによる多様性のある情報伝搬シミュレーションモデルの提案

    Eiki Kitajima, Keiki Takadama, Akinori Murata, Fumito Uwano

    HAIシンポジウム講演論文集   2019.3

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  • 報酬の動的変化に適応する通信なしマルチエージェント協調学習のための公平性に基づく内部報酬設定法

    上野史, 高玉圭樹

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)2018   ROMBUNNO.SS0802   2018.11

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  • 複数解探索を考慮した分散型Bat Algorithm

    岩瀬拓哉, 高野諒, 上野史, 佐藤寛之, 高玉圭樹

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)2018   ROMBUNNO.SS0410   2018.11

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  • グリッドネットワーク上の誤報抑制意見共有アルゴリズム

    北島瑛貴, 辰巳嵩豊, 村田暁紀, 上野史, 高玉圭樹

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)2018   ROMBUNNO.SS0413   2018.11

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  • 行動系列分割に基づく不完全なエキスパートからの逆強化学習

    長谷川智, 上野史, 高玉圭樹

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)2018   ROMBUNNO.SS0804   2018.11

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  • 睡眠時無呼吸症候群患者のための無拘束型リアルタイム睡眠段階推定法

    Yusuke Tajima, Fumito Uwano, Tomohiro Harada, Keiki Takadama

    MICT   2018.11

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  • 睡眠時無呼吸症候群患者に対する無拘束型リアルタイム睡眠段階推定法の分析

    田島友祐, 高野諒, 上野史, 原田智広, 高玉圭樹

    電子情報通信学会技術研究報告118 ( 286(MI2018 38-58)(Web) ) 37‐40 (WEB ONLY)   2018.10

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  • 負の報酬生成による環境変化に適応可能な逆強化学習

    長谷川智, 梅内祐太, 上野史, 佐藤寛之, 高玉圭樹, 山口智浩

    知能システムシンポジウム講演資料(CD-ROM)45th   ROMBUNNO.C4‐2   2018.3

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  • SLIM Spacecraft Location Estimation by Crater Matching Based on Similar Triangles and Its Improvement

    石井晴之, 村田暁紀, 上野史, 辰巳嵩豊, 梅内祐太, 高玉圭樹, 原田智広, 鎌田弘之, 石田貴行, 福田盛介, 澤井秀次郎, 坂井真一郎

    航空宇宙技術(Web)17   69‐78(J‐STAGE)   2018

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  • Evaluating and Improving Spacecraft Localization for SLIM Mission in Comprehensive Problems

    上野史, 村田暁紀, 辰巳嵩豊, 高玉圭樹, 鎌田弘之, 石田貴行, 福田盛介, 澤井秀次郎, 坂井真一郎

    宇宙科学技術連合講演会講演集(CD-ROM)62nd   ROMBUNNO.1D11   2018

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  • 動的環境適応に向けた粒子群最適化とカッコウ探索の協働のための情報共有方法の検討

    梅内祐太, 上野史, 佐藤寛之, 高玉圭樹

    進化計算シンポジウム講演資料   2017.12

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  • Searching Multiple Local Optimal Solutions in Multimodal Function by Bat Algorithm based on Novelty Search

    Takuya Iwase, Ryo Takano, Fumito Uwano, Yuta Umenai, Haruyuki Ishii, Hiroyuki Sato, Keiki Takadama

    進化計算シンポジウム講演資料   2017.12

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  • 深層学習による次元圧縮ルールの学習分類子システムにおける初期ルールとしての可能性

    松本和馬, 高野諒, 上野史, 佐藤寛之, 高玉圭樹

    進化計算シンポジウム講演資料   2017.12

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  • 知識の忘却に基づく迷路形状の変化に追従する非通信マルチエージェント強化学習

    上野史, 高玉圭樹

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)2017   ROMBUNNO.SS13‐4   2017.11

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  • 複数解探索を考慮した分散型Bat Algorithm

    岩瀬拓哉, 高野諒, 上野史, 梅内祐太, 石井晴之, 佐藤寛之, 高玉圭樹

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)2017   ROMBUNNO.SS04‐10   2017.11

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  • 環境変化に向けたPSOとCuckoo Searchに基づく解集団混合進化計算

    梅内祐太, 上野史, 佐藤寛之, 高玉圭樹

    進化計算研究会講演資料   2017.9

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  • Well―being Computing:身体的・心理的・社会的健康増進技術と睡眠からの展望

    高玉圭樹, 村田暁紀, 上野史, 田島友祐, 辰巳嵩豊, 原田智広

    人工知能32 ( 1 ) 81‐86   2017.1

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  • Well-being Computing : Towards Physical, Mental, and Social Well-being from Sleep Perspective

    髙玉 圭樹, 村田 暁紀, 上野 史, 田島 友祐, 辰巳 嵩豊, 原田 智広

    人工知能 : 人工知能学会誌 : journal of the Japanese Society for Artificial Intelligence32 ( 1 ) 81 - 86   2017.1

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  • The current location estimation method to tackle shifted craters occurred the altitude and inclination on SLIM spacecraft

    石井晴之, 村田暁紀, 上野史, 辰巳嵩豊, 梅内裕太, 松本和馬, 高玉圭樹, 鎌田弘之, 石田貴行, 福田盛介, 澤井秀次郎, 坂井真一郎

    宇宙科学技術連合講演会講演集(CD-ROM)61st   ROMBUNNO.1C10   2017

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  • 可変長遺伝子型進化計算に基づく二輪ローバー型惑星探査機のスタック脱出行動最適化

    上野史, 村田暁紀, 高玉圭樹

    計測自動制御学会システム・情報部門学術講演会講演論文集(CD-ROM)2016   ROMBUNNO.SS02‐6   2016.12

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  • カッコウ探索に基づく複数のダイナミズムを含む動的環境への適応

    梅内祐太, 上野史, 佐藤寛之, 高玉圭樹

    進化計算シンポジウム講演資料   2016.12

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  • 快眠を導く音とは─心拍・呼吸に連動した音の睡眠への影響─

    髙玉 圭樹, 村田 暁紀, 上野 史, 田島 友祐, 原田 智広

    人工知能31 ( 3 )   2016.5

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  • 「超高齢化社会とAI―健康増進支援編―」快眠を導く音とは―心拍・呼吸に連動した音の睡眠への影響―

    高玉圭樹, 村田暁紀, 上野史, 田島友祐, 原田智広

    人工知能31 ( 3 ) 383‐388   2016.5

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  • Exploring Sound Sleep : An Influence on Sleep Quality by Sound Adjusted to Heartbeat and Respiration

    髙玉 圭樹, 村田 暁紀, 上野 史, 田島 友祐, 原田 智広

    人工知能 : 人工知能学会誌 : journal of the Japanese Society for Artificial Intelligence31 ( 3 ) 383 - 388   2016.5

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  • Real-time sleep stage estimation from biological data with trigonometric function regression model

    Tomohiro Harada, Fumito Uwano, Takahiro Komine, Yusuke Tajima, Takahiro Kawashima, Morito Morishima, Keiki Takadama

    AAAI Spring Symposium - Technical ReportSS-16-01 - 07   348 - 353   2016.1

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    Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This paper proposes a novel method to estimate sleep stage in real-time with a non-contact device. The proposed method employs the trigonometric function regression model to estimate prospective heart rate from the partially obtained heart rate and calculates the sleep stage from the estimated heart rate. This paper conducts the subject experiment and it is revealed that the proposed method enables to estimate the sleep stage in realtime, in particular the proposed method has the equivalent estimation accuracy as the previous method that estimates the sleep stage according to the entire heart rate during sleeping.

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  • A Modified Cuckoo Search Algorithm for Dynamic Optimization Problems

    Yuta Umenai, Fumito Uwano, Yusuke Tajima, Masaya Nakata, Hiroyuki Sato, Keiki Takadama

    2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)2016 ( CEC ) 1757 - 1764   2016

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    This paper proposes a simple modification of the Cuckoo Search called CS for a dynamic environment. In this paper, we consider a dynamic optimization problem where the global optimum can be cyclically changed depending on time. Our modified CS algorithm holds good candidates in order to effectively explore the search space near those candidates with an intensive local search. Our first experiment tests the prosed method on a set of static optimization problems, which aims at evaluating the potential performance of the proposed method. Then, we apply it to a dynamic optimization problem. Experimental results on the static problems show that the proposed method derives a better performance than the conventional method, which suggest the proposed method potentially has a good capability of finding a good solution. On the dynamic problem, the proposed method also performs well while the conventional method fails to find a better solution.

    DOI: 10.1109/CEC.2016.7744001

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  • 多峰性関数における局所探索に基づくCuckoo Search Algorithm

    梅内祐太, 上野史, 中田雅也, 佐藤寛之, 高玉圭樹

    進化計算シンポジウム講演資料   2015.12

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  • ジレンマ問題におけるマルチエージェント間協調のための内部報酬推算

    上野史, 建部尚紀, 中田雅也, 高玉圭樹

    知能システムシンポジウム講演資料(CD-ROM)42nd   ROMBUNNO.F-11   2015.3

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Presentations

  • 動的環境におけるマルチエージェント強化学習―不完全な情報から集団を動かす仕組み― Invited

    Fumito Uwano

    第6回岡山大学AI研究会  2021.3.4 

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    Presentation type:Oral presentation (invited, special)  

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  • 非通信マルチエージェント協調行動学習に向けた目的価値と内部報酬に基づく強化学習

    上野 史

    関係論的システムデザイン調査研究会  2018.1.22  下原勝憲

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    Venue:滋賀県 同志社大学 びわこリトリートセンター  

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Industrial property rights

Awards

  • DEIM学生プレゼンテーション賞

    2021.3   Flickrとじゃらんnetを利用した穴場スポットの発見手法

    Hikaru Nomoto, Fumito Uwano, Manabu Ohta

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  • DEIM学生プレゼンテーション賞

    2021.3   ニューラルネットワークを用いた表構造解析の一手法

    Hiroyuki Aoyagi, Teruhito Kanazawa, Atsuhiro Takasu, Fumito Uwano, Manabu Ohta

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  • Student Award in 2020

    2020.3   Research contribution

    Fumito Uwano

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  • SSI Excellent Paper Award

    2019.11   多次元意見共有エージェントネットワークモデルにおける複数の環境情報発信源を考慮した誤報伝搬防止アルゴリズム

    Eiki Kitajima, Akinori Murata, Fumito Uwano, Keiki Takadama

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  • Student Award 2019

    2019.3  

    Fumito Uwano

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  • Student Award in 2018

    2018.3  

    Fumito Uwano

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  • Noshiro Space Event, Noshiro CanSat Award

    2017.8  

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  • Noshiro Space Event, Best Poster Award 1st Place

    2017.8  

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  • Student Award in 2017

    2017.3  

    Fumito Uwano

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  • UNISON Oral Presentation Award 2nd Place

    2016.12  

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  • UNISON Poster Presentation Award 1st Place

    2016.12  

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  • ARLISS 2016 UNISEC Award

    2016.9  

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  • Student Award in 2016

    2016.3  

    Takadama Lab, ARLISS, Akinori Murata, Rei Saito, Fumito Uwano

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  • UNISON Poster Presentation Award

    2015.12  

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  • UNISON Oral Presentation Award

    2015.12  

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  • BICT Student Participation Grants

    2015.12   Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach

    Fumito Uwano

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  • ARLISS 2015 Comeback Competition Accuracy Award 1st Place

    2015.9  

    Fumito Uwano(Team, GAIA in, Takadama La

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  • ARLISS 2015 Comeback Competition Technology Award Comeback Algorithm

    2015.9  

    Fumito Uwano(Team, GAIA in, Takadama La

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  • ARLISS 2015 Comeback Competition Technology Award Ground Locomotion Mechanism

    2015.9  

    Fumito Uwano(Team, GAIA in, Takadama La

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  • UNISEC Work Shop Best Poster Award

    2014.12  

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  • ARLISS 2014 Comeback Competition Precision Award

    2014.9  

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Research Projects

  • Transfer learning for multi-agent system to adapt to unknown cooperation and environment

    2021.04 - 2024.03

    Ministry of Education, Culture, Sports, Science and Technology-Japan  Grants-in-aid for scientific research  Early-Career Scientists

    Fumito Uwano

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    Authorship:Principal investigator 

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  • Multi-agent reinforcement learning to acquire coordinate policy with different abstraction

    2020.10 - 2022.03

    Ministry of Education, Culture, Sports, Science and Technology-Japan  Grants-in-aid for scientific research  Research activity start-up

    Fumito Uwano

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    Authorship:Principal investigator 

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  • Non-communicative reinforcement learning to cooperate among agents in dynamic environment

    Grant number:17J08724  2017.04 - 2020.03

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for JSPS Fellows  Grant-in-Aid for JSPS Fellows

    Fumito Uwano

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    Authorship:Principal investigator 

    Grant amount:\2500000 ( Direct expense: \2500000 )

    マルチエージェント強化学習(Multi-Agent Reinforcement Learning: MARL)はロボットのような観測した状態に対し適切に振舞う複数の主体(エージェント)が協調的な振舞いを学習し,困難な課題を解決する手法です.しかしながら実用環境では協調的振舞いは変化するため,MARLによる追従は困難です.本研究は,MARLの実環境適用範囲の拡大のための基盤技術確立を目指し,3年間で1,動的変化に追従する協調行動学習法,2,協調行動学習の理論的補強,3,実問題への適用の3つのテーマに取り組みます.平成30年度ではテーマ1,2に取り組み,主に(1)エージェント数,(2)目的状態及び目的数,(3)報酬値3種類の動的変化に追従可能な非通信協調行動学習法の提案及び理論的補強を行いました.また,テーマ3についても(3)実問題解決に向けた不正確なデータを用いた学習法を考案しました.特に本年度は理論的補強に主眼を置き,各提案手法における最適性とそのための条件,そして適用限界を理論的に示しました.加えて(3)については複数の機械学習法を取り入れ,実問題に向けた不正確な情報しか得られない環境における適切な学習法を考案する等,理論を主眼に置きつつMARLを展開し,今後に向けた準備を着々と進めております.課題(1)の成果は国際会議PRIMA2018にて発表しました.また,課題(2)の成果は,(1)のものと合わせて国際会議ECML PKDD2019に投稿中であり,英文ジャーナルJCMSIに現在条件付きで採録が決定しております.また,課題(3)の成果は国内学会SSI2018にてポスター発表を行い,国際ジャーナルMachine Learningへ現在投稿中です.そして課題(4)の成果は国際会議GECCO2018にて発表を行うなど,対外的に高い評価を受けています.

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Class subject in charge

  • Programming (2020academic year) 3rd and 4th semester  - 水1,水2

  • Programming 1 (2020academic year) Third semester  - 水1,水2

  • Programming 2 (2020academic year) Fourth semester  - 水1,水2

  • Exercises on Programming (2020academic year) 1st and 2nd semester  - 水1,水2,水3

  • Exercises on Programming 1 (2020academic year) 1st semester  - 水1,水2,水3

  • Exercises on Programming 2 (2020academic year) Second semester  - 水1,水2,水3

  • English Engineering (2020academic year) 1st semester  - 火5,火6,木1,木2

  • English Engineering (2020academic year) 1st semester  - 火5,火6,木1,木2

  • Engineering Ethics (2020academic year) Fourth semester  - 火1,火2,金5,金6

  • Introduction to Information Processing 2 (2020academic year) Second semester  - 月1,月2

  • Introduction to Information Processing 2 (2020academic year) Second semester  - 木1,木2

  • Technical Writing (2020academic year) Prophase  - その他

  • Technical Presentation (2020academic year) Late  - その他

  • Specific Research of Electronics and Information Systems Engineering (2020academic year) Year-round  - その他

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Academic Activities

  • The 8th workshop on information processing technology for researchers in IoT era

    Planning, management, etc.

    Manabu Ohta, Fumito Uwano  2021.7.20

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    Type:Academic society, research group, etc. 

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