Fast Algorithms for Relational Marginal Polytopes

Fast Algorithms for Relational Marginal Polytopes

Yuanhong Wang, Timothy van Bremen, Juhua Pu, Yuyi Wang, Ondrej Kuzelka

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 4266-4274. https://doi.org/10.24963/ijcai.2021/586

We study the problem of constructing the relational marginal polytope (RMP) of a given set of first-order formulas. Past work has shown that the RMP construction problem can be reduced to weighted first-order model counting (WFOMC). However, existing reductions in the literature are intractable in practice, since they typically require an infeasibly large number of calls to a WFOMC oracle. In this paper, we propose an algorithm to construct RMPs using fewer oracle calls. As an application, we also show how to apply this new algorithm to improve an existing approximation scheme for WFOMC. We demonstrate the efficiency of the proposed approaches experimentally, and find that our method provides speed-ups over the baseline for RMP construction of a full order of magnitude.
Keywords:
Uncertainty in AI: Statistical Relational AI
Machine Learning: Relational Learning