Researcher Database

（Last updated : 2021-04-19 17:29:06）

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TANIGUCHI Tadahiro

Department / Course College of Information Science and Engineering Department of Information Science and Engineering

Title / Position Professor

Other Affiliations
1.	Graduate School of Information Science and Engineering

Academic background
1.	2006/03 Degree Acquisition	│ Kyoto University Doctor of Engineering
2.	～2001/03	Department of Engineering Science │ Faculty of Engineering │ Kyoto University │ Graduated
3.	～2003/03	Master Course │ Department of Precision Engineering │ Graduate School, Division of Engineering │ Kyoto University │ Completed
4.	～2006/03	Doctoral Program (2nd Semester) │ Department of Precision Engineering │ Graduate School, Division of Engineering │ Kyoto University │ Completed

Research activities
1.		The Institute of Systems, Control and Information Engineers
2.		The Society of Instrument and Control Engineers
3.		Japanese Neural Network Society
4.		The Japanese Society for Artificial Intelligence
5.		Japan Society for Fuzzy Theory and Intelligent Informatics
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Research summary
Constructive understanding of an emergent system containing human beings and the technological application The history of engineering brought material wealth to the modern society. As a result citizens fully enjoy the benefits of material wealth in many advanced countries and come to feel the saturation in their bones. On the other hand, the difference between the value that the artifacts can have in the situation where they are separated from human beings and the value that the artifacts can have in the system that contains human beings on the fields where the human beings and artifacts relate with each other. We can say that the difference is the man-machine system's intrinsic value. In automated design of an aircraft, etc. where the exclusion of human beings is ideal and the safety design of an atomic power plant, we cannot eliminate human errors. An intelligence robot can act itself in a theater space from which the audience is shut out but cannot show itself in the street. A commercial robot pet repeats the same thing and we get tired of it in three days. In many cases, the functions and performance of the artifacts are designed so that the users and environment are assumed to be in an average state, in an ideal state and in a preassumed state. However, the real world is naturally dynamic and the human being that is the main element of the system will change through the adaptation and learning by continuously repeating the physical and social interactions with the environment. I have mainly researched the "constructive understanding of an emergent system containing human beings and the technological application" through the modeling of such a system to produce the artifacts that bring the higher value to the users.

Present specialized field
Statistical Science, Cognitive Science, Human interface and interaction, Intelligent Informatics, Soft computing, Intelligent robotics, Kansei informatics, Intelligent mechanics/ Mechanical systems (Keyword：Artificial Intelligence, Machine learning, Semiotics, Symbol Emergence, Smart Grid)

Books
1.	2019/01/17	Developmental Robotics From Babies to Robots │ (Co-authored)
2.	2017/09	그림과 수식으로 배우는 통통 인공지능 │ (Sole-authored)
3.	2012/04	スマートエネルギーネットワーク最前線～新エネルギー促進，制御技術からシステム構築，企業戦略，自治体実証試験まで～電力ルータの開発と最適電力配分 │ ,pp.第１編　第１章　１．３節「電力ルータの開発と最適電力配分」　を担当執筆 (Co-authored)
4.	2009	Implicit Estimation of Another's Intention Based on Modular Reinforcement Learning Machine Learning │ ,pp.381-400 (Co-authored)

Papers
1.	2020/08/24	Spatial Concept-Based Navigation with Human Speech Instructions via Probabilistic Inference on Bayesian Generative Model │ Advanced Robotics │ 34 (19),pp.1213-1228 (Co-authored)
2.	2020/06/17	Integration of imitation learning using GAIL and reinforcement learning using task-achievement rewards via probabilistic graphical model │ Advanced Robotics │ 34 (16),pp.1055-1067 (Co-authored)
3.	2020/02/08	Improved and scalable online learning of spatial concepts and language models with mapping │ Autonomous Robots │ 44 (6),pp.927-946 (Co-authored)
4.	2020/01/22	Neuro-SERKET: Development of Integrative Cognitive System through the Composition of Deep Probabilistic Generative Models │ New Generation Computing │ 38,pp.23-48 (Co-authored)
5.	2020/01/14	Robust Understanding of Robot-directed Speech Commands using Sequence to Sequence with Noise Injection │ Frontiers in Robotics and AI │ 6 (144),pp.1-12 (Co-authored)
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Academic conference presentation
1.	2020/09/15	Multi-Person Pose Tracking Using Sequential Monte Carlo with Probabilistic Neural Pose Predictor (2020 IEEE International Conference on Robotics and Automation (ICRA))
2.	2019/11/07	Construction of a Multimodal Learning Model Based on Integrating Stochastic Models (2019 IEEE/RSJ International Conference on Intelligent Robots and Systems The Workshop on Deep Probabilistic Generative Models for Cognitive Architecture in Robotics 2019)
3.	2019/11/07	Integrating Simultaneous Localization and Mapping with Map Completion Using Generative Adversarial Networks (2019 IEEE/RSJ International Conference on Intelligent Robots and Systems The Workshop on Deep Probabilistic Generative Models for Cognitive Architecture in Robotics 2019)
4.	2019/10/07	Variational Inference MPC for Bayesian Model-based Reinforcement Learning (Conference on Robot Learning (CoRL))
5.	2019/06/27	Impact Analysis of Order of Presentation on Champion Book Selection in Bibliobattle (the 21st HCI International Conference, HCII 2019)
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Works
1.	2020/06/23	Symbol Emergence in Robotics: Integrative Probabilistic Generative Models for Developmental Human-Robot Communication in the Real-world Environment
2.	2019/11/11	Symbol emergence in robotics: towards developmental artificial embodied intelligence
3.	2018/07/25	Constructive Approach towards Symbol Emergence Systems with Cognitive Robotics and Machine Learning
4.	2018/07/15	Symbol Emergence in Robotics: Towards Architecture for Embodied Developmental General Artificial Intelligence
5.	2018/06/09	Symbol Emergence in Robotics Towards Unsupervised Language Acquisition by Robots
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Winning science prize
1.	2013/06/23	IEEE IEEE Intelligent Vehicle 2013 best poster paper award 1st prize (Unsupervised drive topic finding from driving behavioral data)
2.	2007	SICE Annual Conference 2007 International Award Finalist

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