Abstract: 

Building endgame databases is a common practice for boosting the performance of many computer game programs. After databases are constructed, we usually apply compression to save space. In order not to decrease the performance of accessing compressed files, we used block-based compression routines such as gzip. It is usually the case that bigger databases bring more gains. The sizes of the databases are fairly large even after using state-of-the-art compression programs. We discovered that the compression ratios vary a lot when different position indexing methods are used in a raw endgame file. The intuition is that when a continuous chunk of positions has more uniform values, gzip can better compress it than that of the case of having diversified values in this chunk. We report indexing methods that can be upto 79.89% in compared to a naïve indexing one when both are gziped. Our heuristics can be used on other chess-like endgames. 

Vita:

Associate Professor, Dept. of Applied Mathematics, Chung Yuan Christian University, Taiwan (中原大學應用數學系副教授)

Secretary General, Taiwanese Association for Artificial Intelligence (TAAI) (中華民國人工智慧學會秘書長)