Research field (4)：
Intelligent informatics
, Cognitive sciences
, Intelligent informatics
, Intelligent informatics
Research keywords (10)：
Music Information Retrieval
, Interactive Recommendation System
, mastery
, Multiagent Reinforcement Learning
, Reinforcement Learning
, Learning Process
, Human Agent Interaction
, 人工知能
, Intelligent Mechanics and Machine System
, Artificial Intelligence
Research theme for competitive and other funds (15)：
2020 - 2023 Reward occurence probability vector space that Visualizes the distribution of whole learning results of multi-objective reinforcement learning
2016 - 2019 Designing the autonomous learning system by the continuous reinforcement learning agent with the coach
1993 - 1994 Development of co-planar thin film transformer with high frequency transmission characteristics
1992 - 1992 顔表情の計算機による表現.学習と認識
Study on Learning and Evolution in Artificial Life
Study on Learning and Problem Solving
Behavior Based Robot Learning
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Papers (57)：
Tomohiro Yamaguchi. The Explainable Model to Multi-Objective Reinforcement Learning Toward an Autonomous Smart System. Perspectives and Considerations on the Evolution of Smart Systems. 2023. 18-34
Tomohiro Yamaguchi, Yuto Kawabuchi, Shota Takahashi, Yoshihiro Ichikawa, Keiki Takadama. Formalizing Model-Based Multi-Objective Reinforcement Learning With a Reward Occurrence Probability Vector. Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning. 2022. 299-330
Yoshimiki Maekawa, Tomohiro Yamaguchi, Keiki Takadama. Analyzing Early Stage of Forming a Consensus from Viewpoint of Majority/Minority Decision in Online-Barnga. Lecture Notes in Computer Science. 2021. 269-285
Yoshimiki Maekawa, Tomohiro Yamaguchi, Keiki Takadama. Towards Agent Design for Forming a Consensus Remotely Through an Analysis of Declaration of Intent in Barnga Game. Advances in Intelligent Systems and Computing. 2021. 540-546
Tomohiro Yamaguchi, Shota Nagahama, Yoshihiro Ichikawa, Yoshimichi Honma, Keiki Takadama. Model-Based Multi-Objective Reinforcement Learning by a Reward Occurrence Probability Vector. Advanced Robotics and Intelligent Automation in Manufacturing. 2020. 269-295
Takato Okudo, Keiki Takadama, Tomohiro Yamaguchi. Designing the Learning Goal Space for Human Toward Acquiring a Creative Learning Skill. Human Interface and the Management of Information: Supporting Learning, Decision-Making and Collaboration. 2017. 62-73
山口 智浩. Analyzing human's continuous learning ability toward the intelligent robotics. Proceedings of The Twentieth International Symposium on Artificial Life and Robotics 2015 (AROB 20th 2015), pp.752-757, 2015. 2015
Tomohiro Yamaguchi, Kouki Takernori, Yuki Tamai, Keiki Takadama. Analyzing Human's Continuous Learning Processes with the Reflection sub Task. 2015 10TH ASIAN CONTROL CONFERENCE (ASCC). 2015
Takuma Fujitsuke, Tomohiro Harada, Hiroyuki Sato, Keiki Takadam, Tomohiro Yamaguchi. Sightseeing Plan Recommendation System using Sequential Pattern Mining based on Adjacent Activities. 2015 10TH ASIAN CONTROL CONFERENCE (ASCC). 2015
