Dear guest, welcome to this publication database. As an anonymous user, you will probably not have edit rights. Also, the collapse status of the topic tree will not be persistent. If you like to have these and other options enabled, you might ask Admin for a login account.
| This site is powered by Aigaion - A PHP/Web based management system for shared and annotated bibliographies. For more information visit www.aigaion.de. |
|
Move Prediction in {G}o with the Maximum Entropy Method
| Type of publication: | Inproceedings |
| Citation: | araki:2007 |
| Booktitle: | 2007 IEEE Symposium on Computational Intelligence and Games |
| Year: | 2007 |
| Pages: | 189-195 |
| Abstract: | We address the problem of predicting moves in the board game of Go. We use the relative frequencies of local board patterns observed in game records to generate a ranked list of moves, and then apply the maximum entropy method (MEM) to the list to re-rank the moves. Move prediction is the task of selecting a small number of promising moves from all legal moves, and move prediction output can be used to improve the efficiency of the game tree search. The MEM enables us to make use of multiple overlapping features, while avoiding problems with data sparseness. Our system was trained on 20000 expert games and had 33.9 % prediction accuracy in 500 expert games. |
| Keywords: | |
| Authors | |
| Added by: | [PRT] |
| Total mark: | 0 |
|
Attachments
|
|
|
|
|
Notes
|
|
|
|
|
|
Topics
|
|
|
|
|
