Deep Learning and Block Go

被引：0

作者：

Yen, Shi-Jim ^{[1
]}

Lin, Ching-Nung ^{[1
]}

Cheng, Guan-Lun ^{[1
]}

Chen, Jr-Chang ^{[2
]}

机构：

[1] Natl Dong Hwa Univ, Dept Comp Sci & Informat Engn, Hualien 97401, Taiwan

[2] Chung Yuan Christian Univ, Dept Appl Math, Taoyuan 32023, Taiwan

来源：

2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2017年

关键词：

Block Go; Computer Go; Deep Learning; Deep Convolutional Neural Network; GAMES; STRATEGIES;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Google Deepmind AlphaGo successfully defeated a professional nine dan Go player last March. One of the reasons is that they use deep learning to do a pure pattern-matching approach and predict the next move. In this paper, we use deep learning on the game of Block Go. Block Go is a variance of Go. In this paper, firstly we introduce the complexity of Block Go which is between checkers and Othello. Then we apply Deep Convolutional Neural Network (DCNN) on Block Go. Finally, we show that Block Go is a good research topic for deep learning.

引用

页码：2698 / 2701

页数：4

共 12 条

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