Kyushu University [Kazuki Yoshizoe (Professor) Research Institute for Information Technology, Section of Advanced Computational Science]

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Kazuki Yoshizoe
Professor
Section of Advanced Computational Science
Research Institute for Information Technology
Last modified date：2024.04.30

Graduate School

Department of informatics
Graduate School of Information Science and Electrical Engineering

Undergraduate School

Department of Physics
School of Sciences

Department of Electrical Engineering and Computer Science
School of Engineering

Other Organization

Research Institute for Information Technology

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Phone

Academic Degree

Country of degree conferring institution (Overseas)

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ORCID(Open Researcher and Contributor ID)

Total Priod of education and research career in the foreign country

Research

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1.	Xiufeng Yang, Tanuj Kr Aasawat, Kazuki Yoshizoe, Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design., The Ninth International Conference on Learning Representations (ICLR2021), 2021.05.
2.	Kazuki Yoshizoe, Akihiro Kishimoto, Tomoyuki Kaneko, Haruhiro Yoshimoto, Yutaka Ishikawa, Scalable Distributed Monte Carlo Tree Search, Proceedings of The Fourth Annual Symposium on Combinatorial Search (SoCS2011), 4, 180-187, 2011.07.
3.	Kazuki Yoshizoe, Akihiro Kishimoto, Martin Mueller, Lambda Depth-first Proof Number Search and its Application to Go, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-2007), 2404-2409, 2007.01, Thomsen's lambda search and Nagai's depth-first proof-number (DFPN) search are two powerful but very different AND/OR tree search algorithms. Lambda Depth-First Proof Number search (LDFPN) is a novel algorithm that combines ideas from both algorithms. lambda search can dramatically reduce a search space by finding different levels of threat sequences. DFPN employs the notion of proof and disproof numbers to expand nodes expected to be easiest to prove or disprove. The method was shown to be effective for many games. Integrating lambda order with proof and disproof numbers enables LDFPN to select moves more effectively, while preserving the efficiency of DFPN. LDFPN has been implemented for capturing problems in Go and is shown to be more efficient than DFPN and more robust than an algorithm based on classical lambda search..