Lifted Inference with Linear Order Axiom
This work addresses a computational bottleneck in statistical relational learning by extending tractable inference methods, though it is incremental as it builds on prior extensions of the two-variable fragment.
The paper tackles the problem of weighted first-order model counting (WFOMC) for probabilistic inference by proving that adding a linear order axiom to the two-variable fragment with counting quantifiers still allows computation in polynomial time relative to domain size, and presents a dynamic programming algorithm achieving this.
We consider the task of weighted first-order model counting (WFOMC) used for probabilistic inference in the area of statistical relational learning. Given a formula $φ$, domain size $n$ and a pair of weight functions, what is the weighted sum of all models of $φ$ over a domain of size $n$? It was shown that computing WFOMC of any logical sentence with at most two logical variables can be done in time polynomial in $n$. However, it was also shown that the task is $\texttt{#}P_1$-complete once we add the third variable, which inspired the search for extensions of the two-variable fragment that would still permit a running time polynomial in $n$. One of such extension is the two-variable fragment with counting quantifiers. In this paper, we prove that adding a linear order axiom (which forces one of the predicates in $φ$ to introduce a linear ordering of the domain elements in each model of $φ$) on top of the counting quantifiers still permits a computation time polynomial in the domain size. We present a new dynamic programming-based algorithm which can compute WFOMC with linear order in time polynomial in $n$, thus proving our primary claim.