Daisuke Kitakoshi, Hiroyuki Shioya, and Tsutomu Da-te : A Reinforcement
Learning Method Supported by a Bayesian Network, International Journal
of Computing Anticipatory Systems, Vol. 12, pp. 62-72, 2002.
Daisuke Kitakoshi, Hidetoshi Nonaka, and Tsutomu Da-te : An application
of action selection networks adjusting their structures to autonomous mobile
robots, A. M. S. E. Special Issue 2000, pp. 85-95, 2001.
Daisuke Kitakoshi, Soutarou Uehara, Kentarou Suzuki, Masato Suzuki: Exercise-Integrated Cognitive Training Through Motion-Controlled Gaming: A Pilot Study with Older Adults, Proc. 2025 Conference on Technologies and Applications of Artificial Intelligence (TAAI2025), Taipei, Taiwan, 2025.
Masato Suzuki, Daisuke Kitakoshi: Topological Structure Analysis of Character Images Using Elliptical and CHD Filtrations, Proc. 2025 Conference on Technologies and Applications of Artificial Intelligence (TAAI2025), Taipei, Taiwan, 2025.
Tomoshi Iiyama, Daisuke Kitakoshi, Masato Suzuki: A Method to Optimize
Food Banks’ Food Delivery Schedules: A Preliminary Study, The 2nd International
Symposium on Engineering and Technology, Huisun, Taiwan, 2020. 【Best Presentation Award受賞】
Haruki Eguchi, Senri Yoshikawa, Akihiro Yamashita, Daisuke Kitakoshi, Yihsin
Ho, Yasunari Fujimoto, Toru Yamaguchi, Makoto Kanke and Tomomasa Sato:
Analysis of pyroelectric sensor data installed in the homes of elderly
individuals, Proc. The First IEEE International Symposium on Artificial
Intelligence for ASEAN Development (ASEAN-AI 2018), Phuket, Thailand, 2018.
Daisuke Kitakoshi, Shunya Toda, and Masato Suzuki : A Validity Analysis of a User Model Employed in a Research Activity Support
System, Proceedings of Joint 8th International Conference on Soft Computing and
Intelligent Systems and 17th International Symposium on Advanced Intelligent
Systems (SCIS & ISIS 2016), pp. 591-597, Sapporo, Japan, 2016.
Uthai Phommasak, Daisuke Kitakoshi, and Hiroyuki Shioya : An adaptation
system to unknown environment by modifying the parameters of the profit-sharing
method and mixture probability, Proceedings of International Workshop on
Advanced Computational Intelligence and Intelligent Informatics (IWACIII2011),
GS2-1, Suzhou (China), 2011.
Yusuke Tanahashi, Daisuke Kitakoshi, and Ryohei Nakano : Nominally Piecewise
Multiple Regression Using a Four-Layer Perceptron, Lecture Notes in Computer
Science, Vol.4694, pp. 218-226, 2007. [書誌情報]
Daisuke Kitakoshi, Hiroyuki Shioya, and Ryohei Nakano : A Policy-Improving
System with a Mixture of Bayesian Networks Adapting Agents to Continuously
Changing Environments, Proceedings of SICE-ICASE International Joint Conference
2006 (SICE-ICCAS 2006), Busan (Korea), 2006.
Daisuke Kitakoshi, Hiroyuki Shioya, and Ryohei Nakano : Stochastic Information
Expressed in an Exponential Mixture Model of Bayesian Networks, Proceedings
of Joint 3rd International Conference on Soft Computing and Intelligent
Systems & 7th International Symposium on advanced Intelligent Systems
(SCIS & ISIS 2006), Tokyo (Japan), 2006.
Yusuke Tanahashi, Kazumi Saito, Daisuke Kitakoshi, and Ryohei Nakano :
Finding Nominally Conditioned Multivariate Polynomials Using a Four-Layer
Perceptron Having Shared Weights, Lecture Notes in Computer Science, Vol.
4252, 969-976, 2006.[書誌情報]
Daisuke Kitakoshi, Hiroyuki Shioya, and Ryohei Nakano : A System Improving
Reinforcement Learning Agents' Policies with two types of Mixture Models
of Bayesian Networks, 2005 International Conference on Intelligent Technologies
and Applied Statistics Proceeding, Taipei (Taiwan), 2005.
Daisuke Kitakoshi, Masakazu Hara, Takashi Uozumi, Hiroshi Kubo : Construction
of a System Estimating Emotion and Degree of Fatigue by using Voice Information,
2004 Sino-Japan Symposium on KANSEI & Artificial Life, Beijing (China),
2004.
Daisuke Kitakoshi, Hiroyuki Shioya, and Masahito Kurihara : A Reinforcement
Learning System by using a Mixture Model of Bayesian Network, SICE Annual
Conference, Fukui (Japan), 2003.
Daisuke Kitakoshi, Hiroyuki Shioya, and Masahito Kurihara : Analysis of
a Method Improving Reinforcement Learning Agent's Policies, Third International
Conference on Intelligent Technologies, Hanoi (Vietnam), 2002.
Daisuke Kitakoshi, Hiroyuki Shioya, and Tsutomu Da-te : A Reinforcement
Learning Method Supported by a Bayesian Network, Fifth International Conference
on Computing Anticipatory Systems, Liege (Belgium), 2001.
Daisuke Kitakoshi, Hidetoshi Nonaka, and Tsutomu Da-te : An application
of action selection networks adjusting their structures to autonomous mobile
robots, MS'2000-International Conference on Modelling and Simulation, Las
Palmas (Spain), 2000.
Daisuke Kitakoshi: Development of an Intelligent Dialogue Agent as a Component
of Comprehensive Preventive Care System, The 3rd International Symposium
on Engineering and Technology (ISET2021: 第三屆國際工程與科技研討會), National Chung
Hsing University, Taichung (Taiwan), Nov. 19, 2021.
Daisuke Kitakoshi: Distinctive Research Trends of Artificial Intelligence
in Japan, The 24th Taiwan Academic Newtork Conference (TANET2018: 臺灣網際網路研討會),
National Central University, Taoyuan (Taiwan), Oct. 26, 2018.
招待論文
北越大輔: 東京高専における社会実装教育：フードバンクとの連携によるシステム開発事例紹介, 日本高専学会誌, Vol. 25, No. 4,
pp. 3-10, 2020.
招待講演（国外）
Daisuke Kitakoshi: Introduction of AI Application Cases to Preventive Care -Relationships between Demographic Aging/Declining Birthrate & AI-, National Taipei University, Taipei (Taiwan), Mar. 22, 2024.
Daisuke Kitakoshi: Current Situation about World's Demographic Aging and
AI-based Preventive Care Systems for Older Adults, National Taipei University,
Taipei (Taiwan), Mar. 15, 2019.
Daisuke Kitakoshi: Chracteristic of Super-Aged Society in Japan and Preventive
Care System for Older Adults Aging, National Chun Hsing University, Taichung
(Taiwan), Mar. 6, 2019.
Daisuke Kitakoshi: Current Status & Distinctive Trends of Artificial
Intelligence Research in Japan, National Chi Nan University, Nantou (Taiwan),
Jan. 4, 2019.
Daisuke Kitakoshi: Flexibility and Reactivity in Adaptive Learning Systems,
National Chi Nan University, Nantou (Taiwan), Jun. 8, 2018.
Daisuke Kitakoshi: Hybrid Adaptive Learning System based on Reinforcement
Learning and Mixture Probability, The 3rd International Conference on Fuzzy
Systems and Data Mining (FSDM2017), National Dong Hwa University, Hualien
(Taiwan), Nov. 25, 2017.
Niko Haapalainen, Tomoshi Iiyama, Kota Uematsu, Daisuke Kitakoshi, Masato
Suzuki : Logistics System Utilizing Reinforcement Learning to Optimize
Shipping Costs for Food Welfare Facilities -A Temporary Solution in a Trial
Environment-, 情報処理学会第82回全国大会, 石川, Mar. 2020.