论文标题
复杂的马尔可夫逻辑网络：表现力和升加性
Complex Markov Logic Networks: Expressivity and Liftability
论文作者
论文摘要
我们研究马尔可夫逻辑网络（MLN）的表现力。我们介绍了使用复杂值的重量的复杂MLN，并且我们表明，与具有实价重量的标准MLN不同，复杂的MLN具有完全表达的。然后，我们观察到可以使用具有复杂权重的加权一阶模型计数（WFOMC）来计算离散的傅立叶变换，并使用此观察值来设计用于计算关系边缘多面体的算法，该算法对WFOMC Oracle的调用比最近的算法要少得多。
We study expressivity of Markov logic networks (MLNs). We introduce complex MLNs, which use complex-valued weights, and we show that, unlike standard MLNs with real-valued weights, complex MLNs are fully expressive. We then observe that discrete Fourier transform can be computed using weighted first order model counting (WFOMC) with complex weights and use this observation to design an algorithm for computing relational marginal polytopes which needs substantially less calls to a WFOMC oracle than a recent algorithm.
