Constraint Processing in Lifted Probabilistic Inference

Abstract

First-order probabilistic models combine representational power of first-order logic with graphical models. There is an ongoing effort to design lifted inference algorithms for first-order probabilistic models. We analyze lifted inference from the perspective of constraint processing and, through this viewpoint, we analyze and compare existing approaches and expose their advantages and limitations. Our theoretical results show that the wrong choice of constraint processing method can lead to exponential increase in computational complexity. Our empirical tests confirm the importance of constraint processing in lifted inference. This is the first theoretical and empirical study of constraint processing in lifted inference.

Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)

Cited by in corpus (6)

• Probabilistic Theorem Proving
• Lifted Variable Elimination: Decoupling the Operators from the Constraint Language
• Towards Completely Lifted Search-based Probabilistic Inference
• Lifted Variable Elimination for Probabilistic Logic Programming
• Why is Compiling Lifted Inference into a Low-Level Language so Effective?
• Lifted Graphical Models: A Survey