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.
Get full access to this article
View all access options for this article.
References
1.
Chaslot, G.M.J.-B., Winands, M.H.M., van den Herik, J., Uiterwijk, J.W.H.M. & Bouzy, B. (2008). Progressive strategies for Monte-Carlo tree search. New Mathematics and Natural Computation, 4, 343–357. doi:10.1142/S1793005708001094.
2.
Collobert, R., Kavukcuoglu, K. & Farabet, C. (2011). Torch7: A Matlab-like environment for machine learning. BigLearn, NIPS Workshop.
3.
Gelly, S. & Silver, D. (2011). Monte-Carlo tree search and rapid action value estimation in computer Go. Artificial Intelligence, 175(11), 1856–1875. ISSN 0004-3702. Available at http://www.sciencedirect.com/science/article/pii/S000437021100052X. doi:10.1016/j.artint.2011.03.007.
4.
Gelly, S., Wang, Y., Munos, R. & Teytaud, O. (2006). Modification of UCT with patterns in Monte-Carlo Go. Technical report.
5.
Keskar, N.S., Mudigere, D., Nocedal, J., Smelyanskiy, M. & Tang, P.T.P. (2016). On large-batch training for deep learning: Generalization gap and sharp minima. CoRR. Available at http://arxiv.org/abs/1609.04836.
6.
Lin, C.-N., Yen, S.-J. & Chen, J.-C. (2017). Accelerate parallel deep learning inferences with MCTS in the game of Go. Game Programming Workshop 2017, 2017, 131–137.
7.
Maddison, C.J., Huang, A., Sutskever, I. & Silver, D. (2015). Move evaluation in go using deep convolutional neural networks. ArXiv preprint. Available at https://arxiv.org/pdf/1412.6564.pdf.
8.
Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Driessche, van den Schrittwieser J, G., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T. & Hassabis, D. (2016). Mastering the game of Go. with Deep Neural Networks and Tree Search. Nature, 529(7587), 484–489.
9.
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., Driessche, van den Graepel T, G. & Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550, 354–359. doi:10.1038/nature24270.
10.
Tian, Y. & Zhu, Y. (2015). Better computer Go player with neural network and long-term prediction. ArXiv preprint. Available at arXiv:1511.06410.
11.
van den Herik, J., Uiterwijk, J.W. & van Rijswijck, J. (2002). Games solved: Now and in the future. Artificial Intelligence, 134(1), 277–311. ISSN 0004–3702. Available at http://www.sciencedirect.com/science/article/pii/S0004370201001527.
