Lifted Inference in 2-Variable Markov Logic Networks with Function and Cardinality Constraints Using Discrete Fourier Transform
arXiv:2006.03432v22 citations
Originality Synthesis-oriented
AI Analysis
This addresses a specific inference challenge in probabilistic logical models, but it is incremental as it builds on prior work.
The paper tackled the problem of inference in 2-variable Markov logic networks with cardinality and function constraints, showing that it is domain-liftable by using existing algorithms and discrete Fourier transform.
In this paper we show that inference in 2-variable Markov logic networks (MLNs) with cardinality and function constraints is domain-liftable. To obtain this result we use existing domain-lifted algorithms for weighted first-order model counting (Van den Broeck et al, KR 2014) together with discrete Fourier transform of certain distributions associated to MLNs.