First-Order Decomposition Trees
This work addresses a foundational gap for researchers in probabilistic inference and AI, providing a theoretical framework to analyze lifted inference complexity, though it is incremental as it builds on existing propositional methods.
The paper tackles the lack of formal structures for characterizing lifted inference in probabilistic logical models by introducing FO-dtrees, which extend propositional decomposition trees to the first-order level, enabling a theoretical analysis of complexity through lifted width.
Lifting attempts to speed up probabilistic inference by exploiting symmetries in the model. Exact lifted inference methods, like their propositional counterparts, work by recursively decomposing the model and the problem. In the propositional case, there exist formal structures, such as decomposition trees (dtrees), that represent such a decomposition and allow us to determine the complexity of inference a priori. However, there is currently no equivalent structure nor analogous complexity results for lifted inference. In this paper, we introduce FO-dtrees, which upgrade propositional dtrees to the first-order level. We show how these trees can characterize a lifted inference solution for a probabilistic logical model (in terms of a sequence of lifted operations), and make a theoretical analysis of the complexity of lifted inference in terms of the novel notion of lifted width for the tree.