Updated on 2024/04/16

写真a

 
UWANO Fumito
 
Organization
Faculty of Environmental, Life, Natural Science and Technology Assistant Professor
Position
Assistant Professor
Contact information
メールアドレス
Profile
Student in the doctoral program of the University of Electro-Communications from April 2017 to March 2020 (and Research fellowship DC1 for young scientists in Japan Society for the Promotion of Science)

Uncommunicative Multi-Agent Reinforcement Learning on Static and Dynamic Environments
I proposed the method enables agents to learn cooperative policies without any communications, as well as extended it to dynamic environments in which some accident and others are happened to achieve agents' cooperation under the situation communication delay and it's incorrect information as the real world problems. The results to apply the proposed method to maze problems derived that it can get the maximum rewards as the fastest than Q-learning.

Assistant Professor in Okayama University from April 2020 to present

Multi-Agent Reinforcement Learning to Acquire Cooperative Policy with Different Abstraction
I propose the cooperative method to solve the hetero informatic problem by which agents with different resolutions. Concretely, the proposed method designs agents to control the inputs' abstractions as well as manage the abstractions along to their resolutions. It makes them learn cooperative policies in that problem.

Transfer Learning for Multi-Agent System to Adapt to Unknown Cooperation and Environment
I propose the method for agents to learn cooperative policy on unknown environment, as well as unknown cooperative policy. Concretely, the proposed method designs new primitive cooperative policies based on learned knowledges to conbine them and learn on the combined policies. it makes them create unknown cooperative policies and learn cooperative policies on the unknown environment.

External link

Degree

  • Ph. D. (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

  • Data Engineering

  • Natural Language Processing

  • Digital Libraries

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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  • 私立攻玉社高等学校    

    2008.4 - 2011.3

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    Country: Japan

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Research History

  • Okayama University   Faculty of Environmental, Life, Natural Science and Technology   Assistant Professor

    2023.4

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    Country:Japan

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  • Queensland University of Technology   Faculty of Engineering   Visiting Fellow

    2022.6 - 2022.11

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    Country:Australia

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  • Okayama University   Faculty of Natural Science and Technology   Assistant Professor

    2021.4 - 2023.3

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    Country:Japan

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  • Okayama University   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 Database Society of Japan

    2021.1

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  • 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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Committee Memberships

  • The Robotics Society of Japan   Paper Review Subcommittee  

    2024.4   

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    Committee type:Academic society

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  • Drone Routing Problem Challenge (AAMAS Competition 2024)   Organizer  

    2024   

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    Committee type:Other

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  • Pacific Rim International Conference on Artificial Intelligence (PRICAI)   Program Committee  

    2024   

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  • International Conference on Principles and Practice of Multi-Agent Systems (PRIMA)   Program Committee  

    2024   

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  • Genetic and Evolutionary Computation Conference   Program Committee  

    2023   

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    Committee type:Other

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  • Information Processing Society of Japan   電気・情報関連学会中国支部連合大会プログラム編成委員  

    2022.4 - 2023.3   

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  • 14th International Conference on Agents and Artificial Intelligence (ICAART 2022)   Session Chair  

    2022.2   

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  • 27th International Symposium on Artificial Life and Robotics (AROB 2022)   Session Chair  

    2022.1   

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  • Information Processing Society of Japan   Journal and JIP editorial board committee  

    2021.6   

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  • Information Processing Society of Japan   Steering committee in Chugoku region  

    2021.6 - 2023.3   

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Papers

  • Cognitive Learning System for Sequential Aliasing Patterns of States in Multistep Decision-Making Reviewed

    Fumito Uwano, Will N. Browne

    Proceedings of Genetic and Evolutionary Computation Conference Companion 2024 (GECCO 2024)   2024.7

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  • Automatic Extraction of User-Centric Aspects for Tourist Spot Recommender Systems Using Reviews in Japanese Invited Reviewed

    Fumito Uwano, Ran'u Kobayashi, Manabu Ohta

    Proceedings of the 26th International Conference on Human-Computer Interaction (HCII 2024)   2024.6

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    Authorship:Lead author   Language:English   Publishing type:Research paper (international conference proceedings)  

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  • Learning Agents for Robotics: Trend and Next Challenge Invited Reviewed

    Fumito Uwano

    Journal of Robotics and Mechatronics   36 ( 3 )   2024.6

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    Authorship:Lead author, Last author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

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  • Inverse Reinforcement Learning with Agents’ Biased Exploration Based on Sub-optimal Sequential Action Data Reviewed

    Fumito Uwano, Satoshi Hasegawa, Keiki Takadama

    Journal of Advanced Computational Intelligence and Intelligent Informatics   28 ( 2 )   380 - 392   2024.3

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.20965/jaciii.2024.p0380

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  • Reward Design for Deep Reinforcement Learning Towards Imparting Commonsense Knowledge in Text-based Scenario Reviewed

    Ryota Kubo, Fumito Uwano, Manabu Ohta

    Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART 2024)   2024.2

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  • An End-to-End Table Structure Analysis Method Using Graph Attention Networks Reviewed

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

    Proceedings of the 25th International Conference on Asia-Pacific Digital Libraries (ICADL 2023)   2023.12

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

    DOI: 10.1007/978-981-99-8088-8_20

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  • BERT-based Incident Sign Detection Using Records of Welfare Facility Reviewed

    Fumito Uwano, Norihisa Matsumoto, Manabu Ohta

    IPSJ Journal   64 ( 11 )   2023.11

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    Authorship:Lead author   Language:Japanese   Publishing type:Research paper (scientific journal)  

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  • Reinforcement Learning in Cyclic Environmental Change for Non-Communicative Agents: A Theoretical Approach Reviewed

    Fumito Uwano, Keiki Takadama

    Explainable and Transparent AI and Multi-Agent Systems (Lecture Notes in Computer Science)   14127   2023.9

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    Authorship:Lead author   Language:English   Publishing type:Research paper (international conference proceedings)  

    DOI: 10.1007/978-3-031-40878-6_9

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  • Hierarchical Frames-of-References in Learning Classifier Systems Reviewed

    Fumito Uwano, Will N. Browne

    Proceedings of the Genetic and Evolutionary Computation Conference 2023 (GECCO 2023)   2023.7

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  • Implicit Cooperative Learning on Distribution of Received Reward in Multi-agent System Reviewed

    Fumito Uwano

    Proceedings of 15th International Conference on Agents and Artificial Intelligence (ICAART 2023)   2023.2

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  • Design of Human-Agent-Group Interaction for Correct Opinion Sharing on Social Media Invited Reviewed

    Fumito Uwano, Daiki Yamane, Keiki Takadama

    Proceedings of 24th International Conference on Human-Computer Interaction (HCII 2022)   2022.6

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)  

    DOI: 10.1007/978-3-031-06424-1_12

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  • Table-structure Recognition Method Consisting of Plural Neural Network Models Reviewed

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

    Proceedings of 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2022)   2022.2

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  • LSTM-based Abstraction of Hetero Observation and Transition in Non-Communicative Multi-Agent Reinforcement Learning Reviewed

    Fumito Uwano

    Proceedings of 14th International Conference on Agents and Artificial Intelligence (ICAART 2022)   2022.2

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  • Policy-oriented Goal Selection in Multi-Agent Reinforcement Learning for Dynamic Environments without Communication Reviewed

    Fumito Uwano

    Proceedings of 27th International Symposium on Artificial Life and Robotics (AROB 2022)   2022.1

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  • Table-structure Recognition Method Consisting of Plural Neural Network Modules.

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

    ICPRAM   542 - 549   2022

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    Publishing type:Research paper (international conference proceedings)   Publisher:SCITEPRESS  

    DOI: 10.5220/0010817700003122

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    Other Link: https://dblp.uni-trier.de/db/conf/icpram/icpram2022.html#AoyagiKTUO22

  • 複雑ネットワークに基づく多次元意見共有モデル上の誤報伝搬防止 Reviewed

    上野 史, 北島 瑛貴, 高玉 圭樹

    人工知能学会論文誌   36 ( 6 )   B-KB2_1 - 12   2021.11

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    Language:Japanese   Publishing type:Research paper (scientific journal)  

    DOI: 10.1527/tjsai.36-6_B-KB2

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  • A Cooperative Learning Method for Multi-Agent System with Different Input Resolutions Reviewed

    Fumito Uwano

    Proceedings of 4th International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR 2021)   2021.9

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

    DOI: 10.1109/isamsr53229.2021.9567835

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  • Sigmoid-based Incorrect Opinion Prevention Algorithm on Multi-Opinion Sharing Model Reviewed

    Fumito Uwano, Eiki Kitajima, Keiki Takadama

    Transactions of the Japanese Society for Artificial Intelligence   36 ( 6 )   2021

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  • 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 Science   1 ( 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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  • 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C   140 ( 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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    Publishing type:Research paper (international conference proceedings)   Publisher:IEEE  

    DOI: 10.1109/CEC.2019.8790087

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

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

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  • 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 Integration   11 ( 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 Integration   11 ( 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

  • 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

  • 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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  • 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 Integration   11 ( 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 SCIENCES   17 ( 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

  • 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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  • 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 Mechatronics   29 ( 5 )   877 - 886   2017.10

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

    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 Informatics   21 ( 5 )   907 - 916   2017.9

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

    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 Informatics   21 ( 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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  • Communication-Less Cooperative Q-Learning Agents in Maze Problem Reviewed

    Fumito Uwano, Keiki Takadama

    INTELLIGENT AND EVOLUTIONARY SYSTEMS, IES 2016   8   453 - 467   2017

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    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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  • 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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    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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  • 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 Technologies   2 ( 8 )   e2   2016.5

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    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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  • 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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    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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  • 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

  • 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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Books

  • Artificial Intelligence for Space Applications

    Keiki Takadama, Fumito Uwano, Yuka Waragai, Iko Nakari, Hiroyuki Kamata, Takayuki Ishida, Seisuke Fukuda, Shujiro Sawai, Shinichiro Sakai( Role: Joint author ,  Artificial Intelligence for Spacecraft Location Estimation based on Craters (Chapter 5))

    CRC Press: Taylor & Francis  2023.12  ( ISBN:9781003366386

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  • 表構造情報を利用した棒グラフの自動生成の一手法

    Ayumu Tagami, Teruhito Kanazawa, Fumito Uwano, Manabu Ohta

    DEIM2024   2024.2

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  • 楽天レビューデータを用いたレビュー推薦の一手法

    He Yijie, Fumito Uwano, Manabu Ohta

    DEIM2024   2024.2

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  • メルカリデータセットを利用した商品価格推定の一手法

    Yutaro Mori, Fumito Uwano, Manabu Ohta

    DEIM2024   2024.2

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  • 粒度の異なる商品比較を実現する評判情報可視化インタフェースの提案

    Shion Itagaki, Fumito Uwano, Manabu Ohta

    DEIM2024   2024.2

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  • 確信度を用いたBERTによる参考文献書誌情報抽出の誤り検出の一手法

    Shumpei Nakayama, Teruhito Kanazawa, Fumito Uwano, Manabu Ohma

    DEIM2024   2024.2

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  • 観点の自動抽出に基づくインタラクティブな観光スポット推薦システム

    Ran'u Kobayashi, Fumito Uwano, Manabu Ohta

    DEIM2024   2024.2

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  • 福祉支援施設の支援記録からのインシデントの予兆検出手法の改良

    Ryuta Yamamoto, Fumito Uwano, Manabu Ohta

    DEIM2024   2024.2

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  • ChatGPTを利用した学術論文読解支援の一手法

    Rikuto Sakai, Teruhito Kanazawa, Fumito Uwano, Manabu Ohta

    DEIM2024   2024.2

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  • 公共交通機関の利用を想定した観光ルート推薦の一手法

    Shun Tanaka, Fumito Uwano, Manabu Ohta

    DEIM2024   2024.2

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  • スパース通信環境における複数ロボットの協調行動学習

    Fumito Uwano

    第24回計測自動制御学会システムインテグレーション部門講演会講演論文集   2023.12

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  • RoBERTaとELECTRAを利用した英文前置詞誤りの検出と修正の一手法

    Satoru Nakatani, Fumito Uwano, Manabu Ohta

    第19回ARG Webインテリジェンスとインタラクション研究会講演論文集   2023.12

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  • 英文前置詞の空所補充問題を用いたBERTとその後継モデルの比較

    Souichiro Matsushita, Fumito Uwano, Manabu Ohta

    第19回ARG Webインテリジェンスとインタラクション研究会講演論文集   2023.12

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  • 専門用語に着目した初学者向け学術論文閲覧支援の検討

    Shunsei Takahashi, Teruhito Kanazawa, Fumito Uwano, Manabu Ohta

    第19回ARG Webインテリジェンスとインタラクション研究会   2023.12

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  • 非通信マルチエージェント強化学習による即時的環境変化の追従性に関する一考察

    Fumito Uwano, Keiki Takadama

    SICE SSI2023   2023.11

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  • Commonsense Knowledge獲得のための強化学習エージェントと環境に関する一考察

    Fumito Uwano, Ryota Kubo, Manabu Ohta

    SICE SSI2023   2023.11

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  • Multi-Agent Reinforcement Learning in Different Granularity of Observations Invited

    Fumito Uwano

    Journal of The Society of Instrument and Control Engineers   2023.2

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  • BERTによる英文前置詞誤りの自動修正手法の提案

    Satoru Nakatani, Fumito Uwano, Manabu Ohta

    DEIM 2023   2023.2

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  • 文の類似度とExtractive QAによる被引用文特定の一手法

    Masayoshi Nishiumi, Teruhito Kanazawa, Fumito Uwano, Manabu Ohta

    DEIM 2023   2023.2

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  • 初学者の論文閲覧支援のための日本語論文からの専門用語抽出の一手法

    Shunsei Takahashi, Teruhito Kanazawa, Fumito Uwano, Manabu Ohta

    DEIM 2023   2023.2

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  • グラフニューラルネットワークを用いたエンドツーエンド表構造解析手法の提案

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

    DEIM 2023   2023.2

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  • 観光レビューを利用した観光スポットの観点の自動抽出

    Ranu Kobayashi, Fumito Uwano, Manabu Ohta

    DEIM 2023   2023.2

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  • PLSAによる観光レビューからの季節性トピック抽出の一手法

    Subaru Narahashi, Fumito Uwano, Manabu Ohta

    DEIM 2023   2023.2

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  • 商品比較のための商品レビューの可視化の一手法

    Shion Itagaki, Fumito Uwano, Manabu Ohta

    DEIM 2023   2023.2

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  • BERTによる参考文献書誌情報抽出の誤り検出の評価

    Shumpei Nakayama, Teruhito Kanazawa, Atsuhiro Takasu, Fumito Uwano, Manabu Ohta

    DEIM 2023   2023.2

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  • ニューラルネットワークによる日本語を含む表の構造解析の一手法

    Ryota Hosoya, Teruhito Kanazawa, Fumito Uwano, Manabu Ohta

    DEIM 2023   2023.2

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  • BERTによる支援記録文章の感情極性推定の一手法

    Ryuta Yamamoto, Norihisa Matsumoto, Fumito Uwano, Manabu Ohta

    DEIM 2023   2023.2

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  • 福祉支援施設の支援記録からのインシデントの予兆検出の一手法

    Norihisa Matsumoto, Fumito Uwano, Manabu Ohta

    第176回データベースシステム研究発表会   2022.12

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  • 表検出を含むエンドツーエンド表構造解析手法の評価

    Hiroyuki Aoyagi, Teruhito Kanazawa, Fumito Uwano, Manabu Ohta

    第18回Webインテリジェンスとインタラクション研究会講演論文集   2022.11

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  • 引用意図を利用した初学者向け学術論文閲覧支援方法の検討

    Masayoshi Nishiumi, Teruhito Kanazawa, Fumito Uwano, Manabu Ohta

    第21回情報科学技術フォーラム(FIT2022)講演論文集   2022.9

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  • A Study on Knowledge Distillation and Transfer by Reward Design in Multi-agent Reinforcement Learning

    Fumito Uwano

    人工知能学会全国大会講演論文集   2022.6

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  • BERTによる英文前置詞誤り修正支援の一手法

    Satoru Nakatani, Fumito Uwano, Manabu Ohta

    第14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   2022.2

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  • 評判情報を用いた代替品推薦の一手法

    Daigo Fujimoto, Fumito Uwano, Manabu Ohta

    第14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   2022.2

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  • 観光レビュー文を用いた穴場スポットの発見

    Hikaru Nomoto, Fumito Uwano, Manabu Ohta

    第14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   2022.2

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  • 福祉支援施設の支援記録を利用したインシデント検出の一手法

    Norihisa Matsumoto, Fumito Uwano, Manabu Ohta

    第14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   2022.2

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  • BERTによる和文の参考文献文字列からの書誌情報抽出の評価

    Shunsei Takahashi, Teruhito Kanazawa, Atsuhiro Takasu, Fumito Uwano, Manabu Ohta

    第14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   2022.2

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  • BERTによる参考文献書誌情報抽出における擬似学習データの有効性評価

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

    第17回Webインテリジェンスとインタラクション研究会   2021.12

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  • 未知の環境に適応する学習エージェント群の知識利用法の検討

    Fumito Uwano

    計測自動制御学会システム・情報部門学術講演会2021講演論文集   2021.11

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  • 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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  • 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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  • 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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  • 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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  • 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   63rd   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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  • 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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  • 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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  • 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 Intelligence   32 ( 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 Intelligence   31 ( 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 Report   SS-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

  • 強化学習における認識と報酬のMissing Linkとは

    Fumito Uwano

    境界と関係性を視座とするシステム学調査研究会2023例会  2023.11.26 

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    Event date: 2023.11.25 - 2023.11.26

    Language:Japanese   Presentation type:Oral presentation (general)  

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  • 獲得報酬の分布に基づくエージェント間の暗黙的協調行動学習とその効果の検証

    Fumito Uwano

    SMASH22 Winter Symposium  2022.2.21 

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    Event date: 2022.2.21

    Language:Japanese   Presentation type:Oral presentation (general)  

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  • 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

    Fumito Uwano

    2021.9.25 

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    Event date: 2021.9.25 - 2021.9.26

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  • 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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  • マルチエージェント強化学習における知識とその境界 Invited

    Fumito Uwano

    第69回自律分散システム部会研究会「若手中心とした模倣学習・強化学習」  2022.12.13 

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  • Reward-based Cooperative Control in Multi-agent Deep Reinforcement Learning and That Future Plan Invited

    Fumito Uwano

    Cypher Workshop (February)  2022.2.22 

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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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Awards

  • Student Presentation Award

    2024.3   DEIM2024   福祉支援施設の支援記録からのインシデントの予兆検出手法の改良

    Ryuta Yamamoto, Fumito Uwano, Manabu Ohta

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  • Student Award

    2023.12   ARG WI2研究会   英文前置詞の空所補充問題を用いたBERTとその後継モデルの比較

    Souichiro Matsushita, Fumito Uwano, Manabu Ohta

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  • Excellent Research Award

    2023.12   ARG WI2研究会   RoBERTaとELECTRAを利用した英文前置詞誤りの検出と修正の一手法

    Satoru Nakatani, Fumito Uwano, Manabu Ohta

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  • Educational Contribution Award

    2022.3   Okayama University  

    Yoshinari Nomura, Fumito Uwano, Sunao Hara, Nobuya Watanabe

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  • 2021年度学術奨励賞 研究奨励賞

    2022.2   The Society of Instrument and Control Engineers   Relation between Abstraction of Coordinate Action and Learning Network Topology in Multi-Agent System

    Fumito Uwano

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  • DEIM学生プレゼンテーション賞

    2021.3   DEIM2021   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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  • DEIM学生プレゼンテーション賞

    2021.3   DEIM2021   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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  • Student Award in 2020

    2020.3   The University of Electro-Communications   Research contribution

    Fumito Uwano

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  • SSI Excellent Paper Award

    2019.11   The Society of Instrument and Control Engineers   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   The University of Electro-Communications  

    Fumito Uwano

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  • Student Award in 2018

    2018.3   The University of Electro-Communications  

    Fumito Uwano

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  • Noshiro Space Event, Noshiro CanSat Award

    2017.8   UNISEC  

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  • Noshiro Space Event, Best Poster Award 1st Place

    2017.8   UNISEC  

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  • Student Award in 2017

    2017.3   The University of Electro-Communications  

    Fumito Uwano

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  • UNISON Oral Presentation Award 2nd Place

    2016.12   UNISEC  

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  • UNISON Poster Presentation Award 1st Place

    2016.12   UNISEC  

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  • ARLISS 2016 UNISEC Award

    2016.9   UNISEC  

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  • Student Award in 2016

    2016.3   The University of Electro-Communications  

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

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  • UNISON Poster Presentation Award

    2015.12   UNISEC  

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  • BICT Student Participation Grants

    2015.12   European Alliance for Innovation (EAI)   Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach

    Fumito Uwano

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  • UNISON Oral Presentation Award

    2015.12   UNISEC  

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  • ARLISS 2015 Comeback Competition Accuracy Award 1st Place

    2015.9   UNISEC  

    Fumito Uwano(Team, GAIA in, Takadama La

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  • ARLISS 2015 Comeback Competition Technology Award Comeback Algorithm

    2015.9   UNISEC  

    Fumito Uwano(Team, GAIA in, Takadama La

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  • ARLISS 2015 Comeback Competition Technology Award Ground Locomotion Mechanism

    2015.9   UNISEC  

    Fumito Uwano(Team, GAIA in, Takadama La

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  • UNISEC Work Shop Best Poster Award

    2014.12   UNISEC  

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  • ARLISS 2014 Comeback Competition Precision Award

    2014.9   UNISEC  

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Research Projects

  • Multi-Agent Reinforcement Learning for Strategic Decision Making Using Multi-Objective Evolutionary Computation

    2024.04 - 2027.03

    Japan society for the promotion of science  Grants-in-Aid for Scientific Research-KAKENHI- Scientific Research (B)  Scientific Research (B)

    Fumito Uwano, Tomohiro Harada, Donghui Lin

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  • Development of Cost Sensitive Meta Data Extractor from Papers and Cyber-Physical Paper Reader in Browser

    2022.04 - 2025.03

    Japan society for the promotion of science  Grants-in-Aid for Scientific Research-KAKENHI- Scientific Research (B)  Scientific Research (B)

    Manabu Ohta, Teruhito Kanazawa, Fumito Uwano

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  • Multi-agent system based onrRelationship between human and robot toward transportation optimization

    2022.04 - 2024.03

    Azbil Yamatake General Foundation  Grants-in-aid for scientific research in 2022 

    Fumito Uwano

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  • Cooperative strategy learning and knowledge evolution for multi-agent system to adapt to dynamism in unknown cooperation and environment

    Grant number:21KK0206  2022 - 2024

    Japan society for the promotion of science  Grants-in-Aid for Scientific Research-KAKENHI- Fund for the Promotion of Joint International Research (Fostering Joint International Research (A))  Fund for the Promotion of Joint International Research (Fostering Joint International Research (A))

    上野 史

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    Grant amount:\6890000 ( Direct expense: \5300000 、 Indirect expense:\1590000 )

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  • Research about building a prediction model for incidents in care support facility

    2021.06 - 2023.02

    Okayama System Service  Collaborative research 

    Manabu Ohta, Fumito Uwano, Takahide Aga

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    Authorship:Coinvestigator(s) 

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  • 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

    Grant number:21K17807  2021.04 - 2024.03

    日本学術振興会  科学研究費助成事業 若手研究  若手研究

    上野 史

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    Grant amount:\4680000 ( Direct expense: \3600000 、 Indirect expense:\1080000 )

    本年度は,マルチエージェント強化学習の未知の協調,未知の環境への適応に向けた,(1)学習結果のモジュール化,(2)知識モジュールに基づく未知の協調行動学習法の提案,(3)未知環境を想定した知識の再構成法の提案の内,主にテーマ(1)(2)を実施した.具体的には,まず,従来提案したエージェント間の通信を介さずに環境変化に追従可能な協調行動学習法を,必要な協調行動が動的変化する迷路問題に適用し,協調の変化に追従可能であるかその性能を分析した.結果として協調の変化に対して各エージェントの目的を適切に変化させてそれに追従し学習することを確認した.また,問題領域が同一であれば必要な協調が異なっても適切に学習可能であることが明らかとなった.本成果により深層強化学習器による複数の協調行動の同時学習の可能性が示唆されており,重要な成果であるといえる.また,Coin Gameと呼ばれる他エージェントの学習目標を推定することで高い利得が得られる問題において,従来手法では他エージェントの情報を基に学習していたが,報酬設計によりそれに基づくことなく学習可能な手法を提案し,その有効性を示した.結果として深層強化学習器では直接的な情報を伴わなくとも未知の協調行動を学習し得ることが明らかとなった.本成果は,知識モジュールを抽出した際にそれを組み合わせることによる効果が示唆されており,本研究の前提を裏付ける点において重要である.これらの成果は,計測自動制御学会システム・情報部門学術講演会2021,およびSMASH22 Winter Symposiumにて発表した.

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  • Multi-agent Reinforcement Learning for Cooperative Policy with Different Abstraction

    Grant number:20K23326  2020.09 - 2022.03

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Research Activity Start-up

    Uwano Fumito

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    Grant amount:\2860000 ( Direct expense: \2200000 、 Indirect expense:\660000 )

    This research analyzed deep reinforcement learning agents’ performance in multiagent system with agents having different resolution in input each other to clarify the neural network can abstract the resolution appropriately. Furthermore, this research extended the previous method which enable agents to learn cooperative policy each other in dynamic environment into deep reinforcement learning to result the agents learned a cooperative policy in multiagent maze problem with agents having different resolution in input. At the end, this research introduced LSTM which can learn in time-sequential data into the proposed method to result that the agents can learn synchronously in that maze problem with environment being extended to dynamic one.

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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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Academic Activities

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

    Role(s):Planning, management, etc.

    Manabu Ohta, Fumito Uwano  2021.7.20

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    Type:Academic society, research group, etc. 

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