Design of a Block Go program using deep learning and Monte Carlo tree search

Article type: Research Article

Authors: Lin, Ching-Nung^{a; *} | Chen, Jr-Chang^{b; **} | Yen, Shi-Jim^{a; *; ***} | Chen, Chan-San^{a; *}

Affiliations: [a] National Dong Hwa University, Taiwan | [b] National Taipei University, Taiwan

Correspondence: [***] Corresponding author. E-mail: sjyen@mail.ndhu.edu.tw.

Note: [*] Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan.

Note: [**] Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 23741, Taiwan.

Abstract: The game of Block Go was invented in 2009 as a simplified version of Go. Block Go is played on a 13 × 13 Go board, and each player has nine blocks. This game is suitable for beginners and is popular in most children Go institutes in Taiwan. In this paper, we apply Deep Convolutional Neural Network and Monte Carlo tree search to develop a Block Go program. As a case study, we describe the program named ILEP, which won the Block Go tournament in the Computer Olympiad 2017.

DOI: 10.3233/ICG-180074

Journal: ICGA Journal, vol. 40, no. 3, pp. 149-159, 2018