Difference between revisions of "Tomoyuki Kaneko"

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* [[Tomoyuki Kaneko]], [[Tetsuro Tanaka]] ('''2012'''). ''[https://www.jstage.jst.go.jp/article/jssst/29/1/29_1_1_75/_article GPSShogi and Assembly of Large Shogi Software with Text Protocol]''. [https://www.jstage.jst.go.jp/browse/jssst/29/1/_contents Computer Software - JSSST Journal, Vol. 29, No. 1]
 
* [[Tomoyuki Kaneko]], [[Tetsuro Tanaka]] ('''2012'''). ''[https://www.jstage.jst.go.jp/article/jssst/29/1/29_1_1_75/_article GPSShogi and Assembly of Large Shogi Software with Text Protocol]''. [https://www.jstage.jst.go.jp/browse/jssst/29/1/_contents Computer Software - JSSST Journal, Vol. 29, No. 1]
 
* [[Kunihito Hoki]], [[Tomoyuki Kaneko]], [[Akihiro Kishimoto]], [[Takeshi Ito]] ('''2013'''). ''Parallel Dovetailing and its Application to Depth-First Proof-Number Search''. [[ICGA Journal#36_1|ICGA Journal, Vol. 36, No. 1]] <ref>[https://en.wikipedia.org/wiki/Dovetailing_%28computer_science%29 Dovetailing (computer science) from Wikipedia]</ref>  
 
* [[Kunihito Hoki]], [[Tomoyuki Kaneko]], [[Akihiro Kishimoto]], [[Takeshi Ito]] ('''2013'''). ''Parallel Dovetailing and its Application to Depth-First Proof-Number Search''. [[ICGA Journal#36_1|ICGA Journal, Vol. 36, No. 1]] <ref>[https://en.wikipedia.org/wiki/Dovetailing_%28computer_science%29 Dovetailing (computer science) from Wikipedia]</ref>  
* [[Kunihito Hoki]], [[Tomoyuki Kaneko]] ('''2014'''). ''[https://www.jair.org/papers/paper4217.html Large-Scale Optimization for Evaluation Functions with Minimax Search]''. [https://www.jair.org/vol/vol49.html JAIR Vol. 49], [https://www.jair.org/media/4217/live-4217-7792-jair.pdf pdf] » [[Automated Tuning]], [[Shogi]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=55084 MMTO for evaluation learning] by [[Jon Dart]], [[CCC]], January 25, 2015</ref>
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* [[Kunihito Hoki]], [[Tomoyuki Kaneko]], [[Daisaku Yokoyama]], [[Takuya Obata]], [[Hiroshi Yamashita]], [[Yoshimasa Tsuruoka]], [[Takeshi Ito]] ('''2013'''). ''[https://ieeexplore.ieee.org/document/6598505/ A System-Design Outline of the Distributed-Shogi-System Akara 2010]''. [https://dblp.uni-trier.de/db/conf/snpd/snpd2013.html SNPD 2013]
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* [[Kunihito Hoki]], [[Tomoyuki Kaneko]] ('''2014'''). ''[https://www.jair.org/papers/paper4217.html Large-Scale Optimization for Evaluation Functions with Minimax Search]''. [https://www.jair.org/vol/vol49.html JAIR Vol. 49], [https://pdfs.semanticscholar.org/eb9c/173576577acbb8800bf96aba452d77f1dc19.pdf pdf] » [[Automated Tuning]], [[Shogi]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=55084 MMTO for evaluation learning] by [[Jon Dart]], [[CCC]], January 25, 2015</ref>
 
==2015 ...==
 
==2015 ...==
 
* [[Yusaku Mandai]], [[Tomoyuki Kaneko]] ('''2015'''). ''LinUCB Applied to Monte Carlo Tree Search''. [[Advances in Computer Games 14]]
 
* [[Yusaku Mandai]], [[Tomoyuki Kaneko]] ('''2015'''). ''LinUCB Applied to Monte Carlo Tree Search''. [[Advances in Computer Games 14]]
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* [[Shogo Takeuchi]], [[Tomoyuki Kaneko]] ('''2015'''). ''[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7336038 Estimating Ratings of Computer Players by the Evaluation Scores and Principal Variations in Shogi]''. [http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7335993 ACIT-CSI]
 
* [[Shogo Takeuchi]], [[Tomoyuki Kaneko]] ('''2015'''). ''[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7336038 Estimating Ratings of Computer Players by the Evaluation Scores and Principal Variations in Shogi]''. [http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7335993 ACIT-CSI]
 
* [[Takahisa Imagawa]], [[Tomoyuki Kaneko]] ('''2016'''). ''Monte Carlo Tree Search with Robust Exploration''. [[CG 2016]]
 
* [[Takahisa Imagawa]], [[Tomoyuki Kaneko]] ('''2016'''). ''Monte Carlo Tree Search with Robust Exploration''. [[CG 2016]]
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* [[Taichi Nakayashiki]], [[Tomoyuki Kaneko]] ('''2018'''). ''Learning of Evaluation Functions via Self-Play Enhanced by Checkmate Search''. [[TAAI 2018]]
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* [[Yusaku Mandai]], [[Tomoyuki Kaneko]] ('''2018'''). ''Alternative Multitask Training for Evaluation Functions in Game of Go''. [[TAAI 2018]]
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* [[Hanhua Zhu]], [[Tomoyuki Kaneko]] ('''2018'''). ''Comparison of Loss Functions for Training of Deep Neural Networks in Shogi''. [[TAAI 2018]]
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* [[Tianhe Wang]], [[Tomoyuki Kaneko]] ('''2018'''). ''Application of Deep Reinforcement Learning in Werewolf Game Agents''. [[TAAI 2018]]
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* [[Hyunwoo Oh]], [[Tomoyuki Kaneko]] ('''2018'''). ''Deep Recurrent Q-Network with Truncated History''. [[TAAI 2018]]
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* [[Yusaku Mandai]], [[Tomoyuki Kaneko]] ('''2019'''). ''RankNet for evaluation functions of the game of Go''. [[ICGA Journal#41_2|ICGA Journal, Vol. 41, No. 2]]
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* [[Tomoyuki Kaneko]], [[Takenobu Takizawa]] ('''2019'''). ''[https://www.semanticscholar.org/paper/Computer-Shogi-Tournaments-and-Techniques-Kaneko-Takizawa/caa062c0d95f15c85251351b20cdd80a2a0e1b82#paper-header Computer Shogi Tournaments and Techniques]''. [[IEEE#TOG|IEEE Transactions on Games]], Vol. 11
  
 
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=References=  
 
=References=  
 
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'''[[People|Up one level]]'''
 
'''[[People|Up one level]]'''
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[[Category:Researcher|Kaneko]]
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[[Category:Shogi Programmer|Kaneko]]

Revision as of 21:23, 2 August 2020

Home * People * Tomoyuki Kaneko

Tomoyuki Kaneko [1]

Tomoyuki Kaneko,
a Japanese computer scientist, and associate professor at Graduate School of the University of Tokyo. His research interests include machine learning in games, and automated feature construction for evaluation functions of general game players. He is co-author of the open source Shogi program GPS Shogi [2], available under GPL version 2 or later. In April 2013, GPS Shogi, running on a computer cluster of 700 PCs in the University of Tokyo, beat Hiroyuki Miura, one of the Top-10 professional Shogi players [3] [4].

See also

Selected Publications

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External Links

References

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