%0 Generic %1 maddison2014evaluation %A Maddison, Chris J. %A Huang, Aja %A Sutskever, Ilya %A Silver, David %D 2014 %K 2014 arxiv cnn deep-learning evolutionary %T Move Evaluation in Go Using Deep Convolutional Neural Networks %U http://arxiv.org/abs/1412.6564 %X The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep convolutional networks can be used to directly represent and learn this knowledge. We train a large 12-layer convolutional neural network by supervised learning from a database of human professional games. The network correctly predicts the expert move in 55% of positions, equalling the accuracy of a 6 dan human player. When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional search program GnuGo in 97% of games, and matched the performance of a state-of-the-art Monte-Carlo tree search that simulates a million positions per move.

@misc{maddison2014evaluation, abstract = {The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep convolutional networks can be used to directly represent and learn this knowledge. We train a large 12-layer convolutional neural network by supervised learning from a database of human professional games. The network correctly predicts the expert move in 55% of positions, equalling the accuracy of a 6 dan human player. When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional search program GnuGo in 97% of games, and matched the performance of a state-of-the-art Monte-Carlo tree search that simulates a million positions per move.}, added-at = {2018-05-01T09:37:11.000+0200}, author = {Maddison, Chris J. and Huang, Aja and Sutskever, Ilya and Silver, David}, biburl = {https://www.bibsonomy.org/bibtex/21e2eb596d61001ace93bd2fc9ee3e6c1/achakraborty}, description = {[1412.6564] Move Evaluation in Go Using Deep Convolutional Neural Networks}, interhash = {545a33da64011919396aec8759682e80}, intrahash = {1e2eb596d61001ace93bd2fc9ee3e6c1}, keywords = {2014 arxiv cnn deep-learning evolutionary}, note = {cite arxiv:1412.6564Comment: Minor edits and included captures in Figure 2}, timestamp = {2018-05-01T09:37:11.000+0200}, title = {Move Evaluation in Go Using Deep Convolutional Neural Networks}, url = {http://arxiv.org/abs/1412.6564}, year = 2014 }