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.

翻译：提升概率推理利用概率模型中的对称性，从而在域规模下实现高效的概率推理。要应用提升推理，首先需要获得提升表示，而当前最先进的方法是基于所谓的颜色传递算法。然而，颜色传递算法受限于特定的推理算法，且在构建提升表示时忽略了因子的可交换性。本文提出了一种改进的颜色传递算法，该算法利用逻辑变量构建与特定推理算法无关的提升表示，同时在线下步骤中充分利用因子的可交换性。与现有技术相比，我们提出的算法能高效检测更多对称性，从而大幅提升压缩率，并在应用生成的模型进行概率推理时显著加快在线查询时间。