New Liftable Classes for First-Order Probabilistic Inference
This work advances lifted probabilistic inference for relational domains, offering more efficient solutions for AI systems dealing with large-scale relational data.
The paper tackles the problem of intractable probabilistic inference in statistical relational models by showing that the domain recursion inference rule, previously thought redundant, significantly extends the range of models for which lifted inference runs in polynomial time, including solving the open S4 problem and achieving exponential speedup in certain theories.
Statistical relational models provide compact encodings of probabilistic dependencies in relational domains, but result in highly intractable graphical models. The goal of lifted inference is to carry out probabilistic inference without needing to reason about each individual separately, by instead treating exchangeable, undistinguished objects as a whole. In this paper, we study the domain recursion inference rule, which, despite its central role in early theoretical results on domain-lifted inference, has later been believed redundant. We show that this rule is more powerful than expected, and in fact significantly extends the range of models for which lifted inference runs in time polynomial in the number of individuals in the domain. This includes an open problem called S4, the symmetric transitivity model, and a first-order logic encoding of the birthday paradox. We further identify new classes S2FO2 and S2RU of domain-liftable theories, which respectively subsume FO2 and recursively unary theories, the largest classes of domain-liftable theories known so far, and show that using domain recursion can achieve exponential speedup even in theories that cannot fully be lifted with the existing set of inference rules.