Hongting Niu

Qiaolan Meng, Juhua Pu, Hongting Niu et al.

Knowledge compilation transforms logical theories into circuit representations that support efficient reasoning. We study this problem for propositional groundings of FO2, the two-variable fragment of first-order logic over finite domains. Given an FO2 sentence and a domain of size n, its grounding yields a propositional theory over ground atoms. We ask whether such theories admit compact representations in DNNF-based and related knowledge compilation languages, and whether these can be constructed efficiently, both with respect to the domain size n for a fixed sentence. We show first that compact compilation is impossible in general: there exists an FO2 sentence whose grounding over a domain of size n requires DNNF size $2^{Ω(n)}$. On the positive side, we develop a two-stage compiler that exploits the symmetries inherent in the propositional groundings of FO2 sentences. It branches on unary and binary types rather than individual ground atoms, in a similar spirit to lifted inferences for probabilistic relational models. Moreover, it optimizes the compilation process by efficiently identifying and caching residual subproblems that are equivalent with respect to future extensions. Experiments show the practical efficiency of our approach, which often produces smaller circuits and compiles faster than straightforward grounding-based baselines.

Qiaolan Meng, Juhua Pu, Hongting Niu et al.

We study the model enumeration problem of the function-free, finite domain fragment of first-order logic with two variables ($FO^2$). Specifically, given an $FO^2$ sentence $Γ$ and a positive integer $n$, how can one enumerate all the models of $Γ$ over a domain of size $n$? In this paper, we devise a novel algorithm to address this problem. The delay complexity, the time required between producing two consecutive models, of our algorithm is quadratic in the given domain size $n$ (up to logarithmic factors) when the sentence is fixed. This complexity is almost optimal since the interpretation of binary predicates in any model requires at least $Ω(n^2)$ bits to represent.