Constraint Processing in Lifted Probabilistic Inference
This foundational study addresses computational efficiency issues for researchers in probabilistic AI, though it is incremental as it builds on existing lifted inference approaches.
The paper tackles the problem of analyzing and comparing constraint processing methods in lifted probabilistic inference, showing that poor choices can cause exponential computational complexity increases, as confirmed by empirical tests.
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.