Tomoyuki Kaneko

发表

Visualization and Adjustment of Evaluation Functions Based on Evaluation Values and Win Probability

Kazunori Yamaguchi, Shogo Takeuchi, Tomoyuki Kaneko, 2007, AAAI.

Holder for tires and method for gripping the same

Tomoyuki Kaneko, Yoshiaki Hirata, Takao Kokubu, 2000 .

Evaluation of Monte Carlo tree search and the application to Go

Kazunori Yamaguchi, Shogo Takeuchi, Tomoyuki Kaneko, 2008, 2008 IEEE Symposium On Computational Intelligence and Games.

Imitation Learning for Playing Shogi Based on Generative Adversarial Networks

Tomoyuki Kaneko, Shanchuan Wan, 2017, 2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

Building Evaluation Functions for Chess and Shogi with Uniformity Regularization Networks

Tomoyuki Kaneko, Shanchuan Wan, 2018, 2018 IEEE Conference on Computational Intelligence and Games (CIG).

Rogue-Gym: A New Challenge for Generalization in Reinforcement Learning

Tomoyuki Kaneko, Yuji Kanagawa, Tomoyuki Kaneko, 2019, 2019 IEEE Conference on Games (CoG).

Comparison of Loss Functions for Training of Deep Neural Networks in Shogi

Tomoyuki Kaneko, Hanhua Zhu, Hanhua Zhu, 2018, 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

Towards Evaluation of Shogi Endgames with Speed of Attack

Tetsuro Tanaka, Tomoyuki Kaneko, Shunsuke Soeda, 2004 .

Monte Carlo Tree Search with Robust Exploration

Tomoyuki Kaneko, Takahisa Imagawa, 2016, Computers and Games.

Application of Deep Reinforcement Learning in Werewolf Game Agents

Tomoyuki Kaneko, Tianhe Wang, Tomoyuki Kaneko, 2018, 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

Learning of Evaluation Functions via Self-Play Enhanced by Checkmate Search

Tomoyuki Kaneko, Taichi Nakayashiki, 2018, 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

Parameter-Free Tree Style Pipeline in Asynchronous Parallel Game-Tree Search

Tetsuro Tanaka, Tomoyuki Kaneko, Shu Yokoyama, 2015, ACG.

Automated Identification of Patterns in Evaluation Functions

Kazunori Yamaguchi, Tomoyuki Kaneko, Satoru Kawai, 2003, ACG.

NUMERICAL MODELLING METHOD OF STATIC COMPACTION GROUTING METHOD AS LIQUEFACTION COUTERMEASURE IN OFFSHORE AIRPORT

Tomoyuki Kaneko, Hidenori Takahashi, Terumi Oohashi, 2012 .

Scalable Distributed Monte-Carlo Tree Search

Akihiro Kishimoto, Yutaka Ishikawa, Tomoyuki Kaneko, 2011, SOCS.

Research of an evaluation method of photovoltaic output and generated energy connected with a power grid

Tomoyuki Kaneko, A. Iga, Y. Ishihara, 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

Application of Deep-RL with Sample-Efficient Method in Mini-games of StarCraft II

Tomoyuki Kaneko, Zhejie Hu, 2019, 2019 International Conference on Technologies and Applications of Artiﬁcial Intelligence (TAAI).

The 2013 IPSJ Best Paper Award：Real Time Commentary System for Shogi

Tomoyuki Kaneko, 2014 .

Real-time confocal two-photon fluorescence microscope using a rotating microlens array

Satoshi Kawata, Tomoyuki Kaneko, Katsumasa Fujita, 1999, Other Conferences.

A System-Design Outline of the Distributed-Shogi-System Akara 2010

Yoshimasa Tsuruoka, Takeshi Ito, Tomoyuki Kaneko, 2013, 2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.

Biological Imaging and Sensing from Basic Techniques to Clinical Application

Tomoyuki Kaneko, Osamu Nakamura, I. Fujimasa, 2004 .

The Global Landscape of Objective Functions for the Optimization of Shogi Piece Values with a Game-Tree Search

Tomoyuki Kaneko, Kunihito Hoki, K. Hoki, 2011, ACG.

RankNet for evaluation functions of the game of Go

Tomoyuki Kaneko, Yusaku Mandai, Tomoyuki Kaneko, 2019, J. Int. Comput. Games Assoc..

Acquiring Strategies for the Board Game Geister by Regret Minimization

Chen Chen, Tomoyuki Kaneko, 2019, 2019 International Conference on Technologies and Applications of Artiﬁcial Intelligence (TAAI).

Large-Scale Optimization for Evaluation Functions with Minimax Search

Tomoyuki Kaneko, Kunihito Hoki, K. Hoki, 2014, J. Artif. Intell. Res..

Evaluation of Game Tree Search Methods by Game Records

Kazunori Yamaguchi, Shogo Takeuchi, Tomoyuki Kaneko, 2010, IEEE Transactions on Computational Intelligence and AI in Games.

Analysis of Evaluation-Function Learning by Comparison of Sibling Nodes

Tomoyuki Kaneko, Kunihito Hoki, 2011, ACG.

Learning of Evaluation Functions to Realize Playing Styles in Shogi

Tomoyuki Kaneko, Shotaro Omori, 2016, PRICAI.

Infinite Connect-Four Is Solved: Draw

Kazunori Yamaguchi, Tetsuro Tanaka, Tomoyuki Kaneko, 2011, ACG.

Research on Evaluation Methods of Generated Energy and Lowering Factors of Photovoltaic Generation System

Tomoyuki Kaneko, Atsushi Iga, Yoshiyuki Ishihara, 2004 .

Automatic Feature Construction and Optimization for General Game Player

Kazunori Yamaguchi, Tomoyuki Kaneko, Satoru Kawai, 2001 .

Exploring the implicit interlayer regulatory mechanism between cells and tissue: Stochastic mathematical analyses of the spontaneous ordering in beating synchronization

Tomoyuki Kaneko, Masahiro Okamoto, Hiroyuki Hamada, 2013, Biosyst..

Compact and high-speed ellipsometer

Yoshiro Yamada, Tomoyuki Kaneko, Takeo Yamada, 1992, Other Conferences.

Improved LinUCT and its evaluation on incremental random-feature tree

Tomoyuki Kaneko, Yusaku Mandai, Tomoyuki Kaneko, 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).

Deep Residual Attention Reinforcement Learning

Tomoyuki Kaneko, Hanhua Zhu, 2019, 2019 International Conference on Technologies and Applications of Artiﬁcial Intelligence (TAAI).

Monte Carlo Go Has a Way to Go

Akihiro Kishimoto, Kenjiro Taura, Tomoyuki Kaneko, 2006, AAAI.

Heterogeneous Multi-task Learning of Evaluation Functions for Chess and Shogi

Tomoyuki Kaneko, Shanchuan Wan, 2018, ICONIP.

Estimating the Maximum Expected Value through Upper Confidence Bound of Likelihood

Tomoyuki Kaneko, Takahisa Imagawa, Tomoyuki Kaneko, 2017, 2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

Compiling Logical Features into Specialized Boolean Networks with Incremental Propagation

Kazunori Yamaguchi, Tomoyuki Kaneko, Satoru Kawai, 2002 .

LinUCB applied to Monte Carlo tree search

Tomoyuki Kaneko, Yusaku Mandai, Tomoyuki Kaneko, 2016, Theor. Comput. Sci..

Enhancements in Monte Carlo tree search algorithms for biased game trees

Tomoyuki Kaneko, Takahisa Imagawa, Tomoyuki Kaneko, 2015, 2015 IEEE Conference on Computational Intelligence and Games (CIG).

Playing Catan with Cross-dimensional Neural Network

Tomoyuki Kaneko, Quentin Gendre, 2020, ICONIP.

Pattern Selection Problem for Automatically Generating Evaluation Functions for General Game Player

Kazunori Yamaguchi, Tomoyuki Kaneko, Satoru Kawai, 2002 .

Application of ProbCut into QuiescenceSearch in Shogi

Tomoyuki Kaneko, Satoru Kawai, Shogo Takeuchi, 2005 .

Parallel Dovetailing and its Application to Depth-First Proof-Number Search

Akihiro Kishimoto, Takeshi Ito, Tomoyuki Kaneko, 2013, J. Int. Comput. Games Assoc..

Dual Lambda Search and Shogi Endgames

Tetsuro Tanaka, Tomoyuki Kaneko, Shunsuke Soeda, 2006, ACG.

Test execution and numerical analyses of static compaction grouting method to reduce improvement ratio

Toshio Endo, Tomoyuki Kaneko, Hidenori Takahashi, 2013 .

Diverse Exploration via InfoMax Options pdf

Tomoyuki Kaneko, Yuji Kanagawa, Tomoyuki Kaneko, 2020, ArXiv.

Compiling Logical Features into Specialized State-Evaluators by Partial Evaluation, Boolean Tables and Incremental Calculation

Kazunori Yamaguchi, Tomoyuki Kaneko, Satoru Kawai, 2000, PRICAI.

Parallel Depth First Proof Number Search

Tomoyuki Kaneko, 2010, AAAI.

Evaluation Functions of Computer Shogi Programs and Supervised Learning Using Game Records( Frontiers of Computer Shogi (5))

Tomoyuki Kaneko, Kaneko Tomoyuki, 2012 .

Research on evaluation methods of generated energy and lowering factors of photovoltaic generation system

Tomoyuki Kaneko, Atsushi Iga, Yoshiyuki Ishihara, 2006 .

Alternative Multitask Training for Evaluation Functions in Game of Go

Tomoyuki Kaneko, Yusaku Mandai, 2018, 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

LinUCB Applied to Monte-Carlo Tree Search

Tomoyuki Kaneko, Yusaku Mandai, Tomoyuki Kaneko, 2015, ACG.

Deep Recurrent Q-Network with Truncated History

Tomoyuki Kaneko, Hyunwoo Oh, 2018, 2018 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

Hierarchical Advantage for Reinforcement Learning in Parameterized Action Space

Tomoyuki Kaneko, Zhejie Hu, Tomoyuki Kaneko, 2021, 2021 IEEE Conference on Games (CoG).

Residual Network for Deep Reinforcement Learning with Attention Mechanism

Tomoyuki Kaneko, Hanhua Zhu, Hanhua Zhu, 2021, J. Inf. Sci. Eng..