Colour Passing Revisited: Lifted Model Construction with Commutative Factors

TL;DR

This paper introduces a modified colour passing algorithm that leverages commutativity of factors and logical variables to create more compact lifted representations, significantly improving inference efficiency.

Contribution

It presents a novel colour passing algorithm that is inference-agnostic and exploits factor commutativity, enhancing symmetry detection and model compression.

Findings

Detects more symmetries than previous methods

Increases model compression significantly

Reduces online inference times

Abstract

Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for tractable probabilistic inference with respect to domain sizes. To apply lifted inference, a lifted representation has to be obtained, and to do so, the so-called colour passing algorithm is the state of the art. The colour passing algorithm, however, is bound to a specific inference algorithm and we found that it ignores commutativity of factors while constructing a lifted representation. We contribute a modified version of the colour passing algorithm that uses logical variables to construct a lifted representation independent of a specific inference algorithm while at the same time exploiting commutativity of factors during an offline-step. Our proposed algorithm efficiently detects more symmetries than the state of the art and thereby drastically increases compression, yielding significantly faster online query times for probabilistic inference when the resulting model is applied.