Name
Abstract | ||
|---|---|---|
Building a strong computer Go player is a longstanding open problem. In this paper we consider the related problem of predicting the moves made by Go experts in professional games. The ability to predict experts' moves is useful, because it can, in principle, be used to narrow the search done by a computer Go player. We applied an ensemble of convolutional neural networks to this problem. Our main result is that the ensemble learns to predict 36.9% of the moves made in test expert Go games, improving upon the state of the art, and that the best single convolutional neural network of the ensemble achieves 34% accuracy. This network has less than 104parameters. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87559-8_11 | ICANN (2) |
Keywords | Field | DocType |
main result,strong computer,longstanding open problem,convolutional neural networks,convolutional neural network,related problem,professional game,single convolutional neural network,test expert,neural network,ensemble learning | Open problem,Computer science,Convolutional neural network,Computer Go,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
5164 | 0302-9743 | 5 |
PageRank | References | Authors |
0.77 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ilya Sutskever | 1 | 25814 | 1120.24 |
| Vinod Nair | 2 | 1658 | 134.40 |