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：提升概率推断通过利用概率模型中的对称性，使得推断在域规模下具有可操作性。为应用提升推断，需先获得提升表示，而当前最先进的技术是所谓的颜色传递算法。然而，该算法受限于特定推断方法，且我们发现在构建提升表示时忽略了因子的交换性。我们提出一种改进的颜色传递算法，通过使用逻辑变量构建独立于特定推断算法的提升表示，同时在离线步骤中利用因子交换性。所提算法比现有技术更高效地检测更多对称性，从而大幅提升压缩效果，当应用所构建模型进行概率推断时，显著加快在线查询时间。