AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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On the Completeness of Lifted Variable Elimination
Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel

Last modified: 2013-06-29

Abstract


Lifting aims at improving the efficiency of probabilistic inference by exploiting symmetries in the model. Various methods for lifted probabilistic inference have been proposed, but our understanding of these methods and the relationships between them is still limited, compared to their propositional counterparts. The only existing theoretical characterization of lifting is a completeness result for weighted first-order model counting. This paper addresses the question whether the same completeness result holds for other lifted inference algorithms. We answer this question positively for lifted variable elimination (LVE). Our proof relies on introducing a novel inference operator for LVE.

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