Compressing Chinese Dark Chess Endgame Databases by Deep Learning

Abstract

Endgame databases can be difficult to use in tree search when database sizes remain large even after compression. Given the same endgame, we discover that the compression ratios vary significantly when using different encoding schemes. The intuition is that when a set of positions mapped into a continuous chunk of segments have similar values, block-based compression libraries such as zlib can yield a better compression ratio than cases where segments contain diversified values. However, finding the optimal encoding scheme by exhaustive enumeration is time-infeasible for endgame databases with a large number of pieces. We propose a novel approach using deep learning to obtain an encoding scheme so that the compression ratio is suitable for practical purposes. Our approach can be applied to chess-like games.