Comparing Lossless Compression Methods for Chess Endgame Data (2024)

by Dave Gomboc, Christian R. Shelton, Andrew S. Miner, and Gianfranco Ciardo

Abstract: Chess endgame tables encode unapproximated game-theoretic values of endgame positions. The speed at which information is retrieved from these tables and their representation size are major limiting factors in their effective use. We explore and make novel extensions to three alternatives (decision trees, decision diagrams, and logic minimization) to the currently preferred implementation (Syzygy) for representing such tables. Syzygy is most compact, but also slowest at handling queries. Two-level logic minimization works well, though performing the compression takes significant time. Decision DAGs and multiterminal binary decision diagrams are both comparable and offer the best querying times, with decision diagrams providing better compression.

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Dave Gomboc, Christian R. Shelton, Andrew S. Miner, and Gianfranco Ciardo (2024). "Comparing Lossless Compression Methods for Chess Endgame Data." European Conference on Artificial Intelligence.

Bibtex citation

@inproceedings{GomSheMinCia24,
     author = "Dave Gomboc and Christian R. Shelton and Andrew S. Miner and Gianfranco Ciardo",
     title = "Comparing Lossless Compression Methods for {C}hess Endgame Data",
     year = 2024,
     booktitle = "European Conference on Artificial Intelligence",
     booktitleabbr = "ECAI",
}

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