Complex Markov Logic Networks: Expressivity and Liftability

2020/02/24 by Ondřej Kuželka, Kuzelka, Ondrej
Computer Science · #Bayesian Modeling and Causal Inference #Machine Learning and Algorithms #Formal Methods in Verification

paper · pdf · doi:10.48550/arxiv.2002.10259

Abstract

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.

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