Compiling Logical Features into Specialized State-Evaluators by Partial Evaluation, Boolean Tables and Incremental Calculation. - Tomoyuki Kaneko; Kazunori Yamaguchi; Satoru Kawai (PRICAI, 2000)

Automated Identification of Patterns in Evaluation Functions. - Tomoyuki Kaneko; Kazunori Yamaguchi; Satoru Kawai (ACG, 2003)

Monte Carlo Go Has a Way to Go. - Haruhiro Yoshimoto; Kazuki Yoshizoe; Tomoyuki Kaneko; Akihiro Kishimoto; Kenjiro Taura (AAAI, 2006)

Dual Lambda Search and Shogi Endgames. - Shunsuke Soeda; Tomoyuki Kaneko; Tetsuro Tanaka (ACG, 2006)

Visualization and Adjustment of Evaluation Functions Based on Evaluation Values and Win Probability. - Shogo Takeuchi; Tomoyuki Kaneko; Kazunori Yamaguchi; Satoru Kawai (AAAI, 2007)

Evaluation of Monte Carlo tree search and the application to Go. - Shogo Takeuchi; Tomoyuki Kaneko; Kazunori Yamaguchi (CIG, 2008)

Parallel Depth First Proof Number Search. - Tomoyuki Kaneko (AAAI, 2010)

Evaluation of Game Tree Search Methods by Game Records. - Shogo Takeuchi; Tomoyuki Kaneko; Kazunori Yamaguchi (IEEE Trans. Comput. Intell. AI Games, 2010)

The Global Landscape of Objective Functions for the Optimization of Shogi Piece Values with a Game-Tree Search. - Kunihito Hoki; Tomoyuki Kaneko (ACG, 2011)

Analysis of Evaluation-Function Learning by Comparison of Sibling Nodes. - Tomoyuki Kaneko; Kunihito Hoki (ACG, 2011)

Infinite Connect-Four Is Solved: Draw. - Yoshiaki Yamaguchi; Kazunori Yamaguchi; Tetsuro Tanaka; Tomoyuki Kaneko (ACG, 2011)

Scalable Distributed Monte-Carlo Tree Search. - Kazuki Yoshizoe; Akihiro Kishimoto; Tomoyuki Kaneko; Haruhiro Yoshimoto; Yutaka Ishikawa (SOCS, 2011)

A System-Design Outline of the Distributed-Shogi-System Akara 2010. - Kunihito Hoki; Tomoyuki Kaneko; Daisaku Yokoyama; Takuya Obata; Hiroshi Yamashita; Yoshimasa Tsuruoka; Takeshi Ito (SNPD, 2013)

Exploring the implicit interlayer regulatory mechanism between cells and tissue: Stochastic mathematical analyses of the spontaneous ordering in beating synchronization. - Hiroyuki Hamada; Fumimasa Nomura; Tomoyuki Kaneko; Kenji Yasuda; Masahiro Okamoto (Biosyst., 2013)

Parallel Dovetailing and its Application to Depth-First Proof-Number Search. - Kunihito Hoki; Tomoyuki Kaneko; Akihiro Kishimoto; Takeshi Ito (J. Int. Comput. Games Assoc., 2013)

Large-Scale Optimization for Evaluation Functions with Minimax Search. - Kunihito Hoki; Tomoyuki Kaneko (J. Artif. Intell. Res., 2014)

Parameter-Free Tree Style Pipeline in Asynchronous Parallel Game-Tree Search. - Shu Yokoyama; Tomoyuki Kaneko; Tetsuro Tanaka (ACG, 2015)

Estimating Ratings of Computer Players by the Evaluation Scores and Principal Variations in Shogi. - Shogo Takeuchi; Tomoyuki Kaneko (ACIT-CSI, 2015)

Enhancements in Monte Carlo tree search algorithms for biased game trees. - Takahisa Imagawa; Tomoyuki Kaneko (CIG, 2015)

Monte Carlo Tree Search with Robust Exploration. - Takahisa Imagawa; Tomoyuki Kaneko (Computers and Games, 2016)

Improved LinUCT and its evaluation on incremental random-feature tree. - Yusaku Mandai; Tomoyuki Kaneko (CIG, 2016)

Learning of Evaluation Functions to Realize Playing Styles in Shogi. - Shotaro Omori; Tomoyuki Kaneko (PRICAI, 2016)

LinUCB applied to Monte Carlo tree search. - Yusaku Mandai; Tomoyuki Kaneko (Theor. Comput. Sci., 2016)

Estimating the Maximum Expected Value through Upper Confidence Bound of Likelihood. - Takahisa Imagawa; Tomoyuki Kaneko (TAAI, 2017)

Imitation Learning for Playing Shogi Based on Generative Adversarial Networks. - Shanchuan Wan; Tomoyuki Kaneko (TAAI, 2017)

Building Evaluation Functions for Chess and Shogi with Uniformity Regularization Networks. - Shanchuan Wan; Tomoyuki Kaneko (CIG, 2018)

Heterogeneous Multi-task Learning of Evaluation Functions for Chess and Shogi. - Shanchuan Wan; Tomoyuki Kaneko (ICONIP (3), 2018)

Alternative Multitask Training for Evaluation Functions in Game of Go. - Yusaku Mandai; Tomoyuki Kaneko (TAAI, 2018)

Learning of Evaluation Functions via Self-Play Enhanced by Checkmate Search. - Taichi Nakayashiki; Tomoyuki Kaneko (TAAI, 2018)

Deep Recurrent Q-Network with Truncated History. - Hyunwoo Oh; Tomoyuki Kaneko (TAAI, 2018)

Application of Deep Reinforcement Learning in Werewolf Game Agents. - Tianhe Wang; Tomoyuki Kaneko (TAAI, 2018)

Comparison of Loss Functions for Training of Deep Neural Networks in Shogi. - Hanhua Zhu; Tomoyuki Kaneko (TAAI, 2018)

Rogue-Gym: A New Challenge for Generalization in Reinforcement Learning. - Yuji Kanagawa; Tomoyuki Kaneko (CoG, 2019)

Acquiring Strategies for the Board Game Geister by Regret Minimization. - Chen Chen; Tomoyuki Kaneko (TAAI, 2019)

Application of Deep-RL with Sample-Efficient Method in Mini-games of StarCraft II. - Zhejie Hu; Tomoyuki Kaneko (TAAI, 2019)

Deep Residual Attention Reinforcement Learning. - Hanhua Zhu; Tomoyuki Kaneko (TAAI, 2019)

RankNet for evaluation functions of the game of Go. - Yusaku Mandai; Tomoyuki Kaneko (J. Int. Comput. Games Assoc., 2019)

Computer Shogi Tournaments and Techniques. - Tomoyuki Kaneko; Takenobu Takizawa (IEEE Trans. Games, 2019)

Playing Catan with Cross-Dimensional Neural Network. - Quentin Gendre; Tomoyuki Kaneko (ICONIP (2), 2020)

Evaluation of Loss Function for Stable Policy Learning in Dobutsu Shogi. - Taichi Nakayashiki; Tomoyuki Kaneko (TAAI, 2020)

Diverse Exploration via InfoMax Options. - Yuji Kanagawa; Tomoyuki Kaneko (CoRR, 2020)

Improving Counterfactual Regret Minimization Agents Training in Card Game Cheat Using Ordered Abstraction. - Cheng Yi; Tomoyuki Kaneko (ACG, 2021)

Hierarchical Advantage for Reinforcement Learning in Parameterized Action Space. - Zhejie Hu; Tomoyuki Kaneko (CoG, 2021)

Maximum Entropy Reinforcement Learning in Two-Player Perfect Information Games. - Taichi Nakayashiki; Tomoyuki Kaneko (SSCI, 2021)

Local Coordination in Multi-Agent Reinforcement Learning. - Fanchao Xu; Tomoyuki Kaneko (TAAI, 2021)

Residual Network for Deep Reinforcement Learning with Attention Mechanism. - Hanhua Zhu; Tomoyuki Kaneko (J. Inf. Sci. Eng., 2021)

Learning Strategies for Imperfect Information Board Games Using Depth-Limited Counterfactual Regret Minimization and Belief State. - Chen Chen; Tomoyuki Kaneko (CoG, 2022)

DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards. - Shanchuan Wan; Yujin Tang; Yingtao Tian; Tomoyuki Kaneko (IJCAI, 2023)

Curiosity-driven Exploration for Cooperative Multi-Agent Reinforcement Learning. - Fanchao Xu; Tomoyuki Kaneko (IJCNN, 2023)

Initial state diversification for efficient AlphaZero-style training. - Yosuke Demura; Tomoyuki Kaneko (J. Int. Comput. Games Assoc., 2024)

The Role of Background Information in Reducing Object Hallucination in Vision-Language Models: Insights from Cutoff API Prompting. - Masayo Tomita; Katsuhiko Hayashi 001; Tomoyuki Kaneko (CoRR, 2025)