Weighted First Order Model Counting (WFOMC) computes the weighted sum of the models of a first order theory on a domain of a given finite size. WFOMC has emerged as a fundamental tool for probabilistic inference. Algorithms for WFOMC that run in polynomial time w.r.t. the domain size are called lifted inference algorithms. Such algorithms have been developed for multiple extensions of FO$^2$(the fragment of First Order Logic with two variables) for the special case of symmetric weight functions. In this paper, instead of developing a specific algorithm, we derive a closed form formula for WFOMC in FO$^2$. The three key advantages of our proposal are: (i) it deals with existential quantifiers without introducing negative weights; (ii) it easily extends to FO$^2$ with cardinality constraints and counting quantifiers (aka C$^2$); finally, (iii) it supports WFOMC for a class of weight functions strictly larger than symmetric weight functions, which can model count distributions, without introducing complex or negative weights.
翻译:加权一等计算模型(WFOMC)计算了某一有限尺寸范围内第一阶理论模型模型的加权总和。 WFOMC已成为概率推论的基本工具。在多角时间运行的WFOMC的算法称为解除推论算法。这种算法是为对称加权函数特例的多重扩展FO$2(第一级逻辑的碎片和两个变量)而开发的。在本文中,我们不是开发一种特定的算法,而是以FOMC的封闭式公式,以2美元计算。我们提案的三个主要优点是:(一) 它涉及不引入负重量的活性量化器;(二) 它很容易扩展到具有基度限制和计数四等分数的FO$2美元(C$2美元);最后,它支持WFOCCMC用于严格大于对称重量函数的类别,它可以不引入复合重量或负重量分布,而不引入负重量的模型。