@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00175340,
 author = {Shi-Jim, Yen and Keng, Wen Li and Chingnung, Lin and Jr-Chang, Chen and Shi-Jim, Yen and Keng, Wen Li and Chingnung, Lin and Jr-Chang, Chen},
 book = {ゲームプログラミングワークショップ2016論文集},
 month = {Oct},
 note = {Block Go is similar to the game of Go. The game is introduced by Pro Zhang Xu at 2009. His purpose is to reduce the complexity of Go and let the game be suitable for children. The complexity of Block Go is around 10^45, which is between checker and Othello. In this paper, we will state the rule Go and analyses the complexity of Block. Then, the Block Go program is implemented with Monte Carlo Tree Search (MCTS), and minorization-maximization pattern database. We also apply Deep Convolutional Neural Network (DCNN) on Block Go. In the future, we will apply transfer learning to improve the DCNN of Block Go, based on the numerous Go game records., Block Go is similar to the game of Go. The game is introduced by Pro Zhang Xu at 2009. His purpose is to reduce the complexity of Go and let the game be suitable for children. The complexity of Block Go is around 10^45, which is between checker and Othello. In this paper, we will state the rule Go and analyses the complexity of Block. Then, the Block Go program is implemented with Monte Carlo Tree Search (MCTS), and minorization-maximization pattern database. We also apply Deep Convolutional Neural Network (DCNN) on Block Go. In the future, we will apply transfer learning to improve the DCNN of Block Go, based on the numerous Go game records.},
 pages = {69--72},
 publisher = {情報処理学会},
 title = {Deep Convolutional Neural Network, Minorization-Maximization Algorithm, and Monte Carlo Tree Search on Block Go},
 volume = {2016},
 year = {2016}
}