Shogi
Learning algorithms
- Demura, Y. and Kaneko, T. “Initial state diversification for efficient alphazero-style training,” ICGA Journal, 46(2) pp. 40-66. 2024, doi: 10.3233/ICG-240255.
- Kaneko, T. and T. Takizawa “Computer Shogi Tournaments and Techniques,” IEEE Transac- tions on Games, Vol. 11, No. 3, pp. 267–274 (2019), DOI: 10.1109/TG.2019.2939259.
- S. Wan and T. Kaneko. Heterogeneous Multi-Task Learning of Evaluation Functions for Chess and Shogi, ICONIP 2018.
- Wan, S. and T. Kaneko “Pos2Pos: Automatic Position-to-Position Translation in Chess-Like Games,” in 23rd Game Programming Workshop, pp. 51–54, 11 (2018).
- S. Wan and T. Kaneko. Building Evaluation Functions for Chess and Shogi with Uniformity Regularization Networks, IEEE CIG 2018
- Zhu, H. and T. Kaneko “Comparison of Loss Functions for Training of Deep Neural Networks in Shogi,” in IEEE Technologies and Applications of Artificial Intelligence, pp. 18–23 (2018), DOI: 10.1109/TAAI.2018.00014.
- K. Hoki and T. Kaneko (2014) “Large-Scale Optimization for Evaluation Functions with Minmax Search”, Volume 49, pages 527-568. JAIR
Playing styles
- S. Omori and T. Kaneko:Learning of Evaluation Functions to Realize Playing Styles in Shogi, LNCS, PRICAI. 367-379.
Denou-sen (2013)
GPS Shogi won against Miura 8-dan (now 9-dan) in April, 2013
