  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.
