Colour Passing Revisited: Lifted Model Construction with Commutative Factors
This work improves lifted probabilistic inference for AI systems by enhancing efficiency in model construction, though it is incremental as it builds on existing state-of-the-art methods.
The paper tackles the problem of constructing lifted representations for probabilistic inference by modifying the colour passing algorithm to exploit commutativity of factors, resulting in more symmetries detected, increased compression, and significantly faster online query times.
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.