Weighted First-Order Model Counting over Ordered Domains

Abstract

The Weighted First-Order Model Counting Problem (WFOMC) asks for the weighted sum of models of a first-order logical sentence over a domain. It is a fundamental problem in statistical relational learning, with applications extending to enumerative combinatorics and graph polynomials. Computing WFOMC for the three-variable fragment is $\mathsf{\#P}_1$-hard, whereas polynomial-time algorithms exist for the two-variable fragment and its extensions by cardinality constraints and counting quantifiers. In this work, we explore computing WFOMC in polynomial time over linearly ordered domains, enabling tractable reasoning across inference scenarios and combinatorial problems involving sequences. Because encoding a linear order in standard first-order logic requires three variables, negating our polynomial-time aspirations, we add a linear order axiom directly to the language. This forces one predicate to impose a total ordering on domain elements. We first prove that WFOMC with the linear order axiom can be solved in time polynomial in the domain size. We then extend this result to ordered domains with access to successor relations. While this holds when successors are explicitly defined via the linear order, we demonstrate an alternative implicit approach where successor relations are part of the axiom. This implicit method exhibits significantly better performance on all tested instances, sometimes providing exponential runtime improvements. Finally, we analyze scenarios with two distinct linear orders. We show that WFOMC over the two-variable fragment with two linear orders is $\mathsf{\#P}_1$-hard. However, we develop a polynomial-time algorithm for WFOMC with one linear order and a successor relation of another, pushing the intractability barrier further, yet still leaving the question of how close to a second full linear order one can get.