Synthesising Recursive Functions for First-Order Model Counting: Challenges, Progress, and Conjectures
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Abstract (arXiv)
First-order model counting (FOMC) is a computational problem that asks to count the models of a sentence in finite-domain first-order logic. In this paper, we argue that the capabilities of FOMC algorithms to date are limited by their inability to express many types of recursive computations. To enable such computations, we relax the restrictions that typically accompany domain recursion and generalise the circuits used to express a solution to an FOMC problem to directed graphs that may contain cycles. To this end, we adapt the most well-established (weighted) FOMC algorithm ForcLift to work with such graphs and introduce new compilation rules that can create cycle-inducing edges that encode recursive function calls. These improvements allow the algorithm to find efficient solutions to counting problems that were previously beyond its reach, including those that cannot be solved efficiently by any other exact FOMC algorithm. We end with a few conjectures on what classes of instances could be domain-liftable as a result.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 7 Jun 2023.
Builds on
9 of 29 references.
References · 9
- 2014Symmetric Weighted First-Order Model Countingcited by 19
- 2020Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifierscited by 16
- 2013Skolemization for Weighted First-Order Model Countingcited by 15
- 2016New Liftable Classes for First-Order Probabilistic Inferencecited by 15
- 2018Weighted model counting beyond two-variable logiccited by 13
- 2020Manthan: A Data Driven Approach for Boolean Function Synthesiscited by 11
- 2021Weighted Model Counting in FO2 with Cardinality Constraints and Counting Quantifiers: A Closed Form Formulacited by 8
- 2020Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetrycited by 2
- 2014Approximate Lifted Inference with Probabilistic Databasescited by 1
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Citing papers per year: 2025: 1.
1 paper.
- 2025Towards Practical First-Order Model Countingcited by 1
- 2025Towards Practical First-Order Model Counting17 Feb 2025