First-Order Model Counting and Sampling

Explore a 37-minute lecture on First-Order Model Counting (FOMC) and its weighted variant, WFOMC, presented by Ondrej Kuzelka from Prague University. Delve into recent developments in WFOMC and Weighted First-Order Model Sampling (WFOMS), including their applications in statistical relational learning, automated solving of enumerative combinatorics problems, and elementary probability theory. Discover how WFOMS could serve as a foundation for a declarative framework for sampling combinatorial structures, extending beyond current programming language libraries. Gain insights into the decade-long research that has identified non-trivial classes of WFOMC problems solvable in polynomial time relative to the number of domain elements. Learn about the potential for WFOMS as a basis for more advanced combinatorial structure sampling, highlighting areas for further research in efficient WFOMS representations.