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Influence diagrams are graphical models used to represent and solve decision-making problems under uncertainty. The solution of an influence diagram, a strategy, is traditionally represented by tables that map histories to actions; it can also be represented by an equivalent strategy tree. We show how to compress a strategy tree into an equivalent and more compact strategy graph, making strategies easier to interpret and understand. We also show how to compress a strategy graph further in exchange for bounded-error approximation.
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This content will become publicly available on October 19, 2025
Comparing Lossless Compression Methods for Chess Endgame Data
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 when the full compression algorithm can be run. Decision DAGs and multiterminal binary decision diagrams are comparable and offer the best querying times, with decision diagrams providing better compression.
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- Award ID(s):
- 2212142
- PAR ID:
- 10539393
- Publisher / Repository:
- European Conference on Artificial Intelligence
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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