Name
Abstract | ||
|---|---|---|
This paper describes the application of comparison training (CT) for automatic feature weight tuning, with the final objective of improving the evaluation functions used in Chinese chess programs. First, we propose an n-tuple network to extract features, since n-tuple networks require very little expert knowledge through its large numbers of features, while simulta-neously allowing easy access. Second, we propose a novel evalua-tion method that incorporates tapered eval into CT. Experiments show that with the same features and the same Chinese chess program, the automatically tuned comparison training feature weights achieved a win rate of 86.58% against the weights that were hand-tuned. The above trained version was then improved by adding additional features, most importantly n-tuple features. This improved version achieved a win rate of 81.65% against the trained version without additional features. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Artificial Intelligence | Computer science,Artificial intelligence,Machine learning |
DocType | Volume | Citations |
Journal | abs/1801.07411 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen-Jie Tseng | 1 | 26 | 8.89 |
| Jr-Chang Chen | 2 | 42 | 15.19 |
| I-Chen Wu | 3 | 208 | 55.03 |
| tinghan wei | 4 | 10 | 7.89 |