Uncertainty in Artificial Intelligence
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Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting
Eric Gribkoff, Guy Van den Broeck, Dan Suciu
In this paper we study lifted inference for the Weighted First-Order Model Counting prob- lem (WFOMC), which counts the assignments that satisfy a given sentence in first-order logic (FOL); it has applications in Statisti- cal Relational Learning (SRL) and Probabilis- tic Databases (PDB). We present several results. First, we describe a lifted inference algorithm that generalizes prior approaches in SRL and PDB. Second, we provide a novel dichotomy result for a non-trivial fragment of FO CNF sentences, showing that for each sentence the WFOMC problem is either in PTIME or #P- hard in the size of the input domain; we prove that, in the first case our algorithm solves the WFOMC problem in PTIME, and in the second case it fails. Third, we present several proper- ties of the algorithm. Finally, we discuss limi- tations of lifted inference for symmetric proba- bilistic databases (where the weights of ground literals depend only on the relation name, and not on the constants of the domain), and prove the impossibility of a dichotomy result for the complexity of probabilistic inference for the en- tire language FOL.
Pages: 280-289
PS Link:
PDF Link: /papers/14/p280-gribkoff.pdf
AUTHOR = "Eric Gribkoff and Guy Van den Broeck and Dan Suciu",
TITLE = "Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "280--289"

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