联合公式的推广提升推理
Extended Lifted Inference with Joint Formulas
摘要：
一阶变量消除（FOVE）算法允许将精确推理直接应用于概率关系模型，并且已被证明大大优于在接地命题模型上应用标准推理方法。尽管如此，FOVE算子可以在限制条件下应用，通常迫使人们诉诸命题推理。本文旨在通过提供两个新的模型转换算子来扩展FOVE的适用性：第一个和主要的是联合公式转换和第二个是不同的计数转换。这些新的操作允许有效的推理方法被直接应用于关系模型，其中迄今为止没有现有的有效方法可以应用。此外，这些功能的帮助下，我们展示了如何适应FOVE提供精确的解决方案，最大预期效用（MEU）查询关系模型的不确定性下的决定。实验评估表明，我们的算法，以提供显着的加速比的替代品。
英文摘要：
The First-Order Variable Elimination (FOVE) algorithm allows exact inference to be applied directly to probabilistic relational models, and has proven to be vastly superior to the application of standard inference methods on a grounded propositional model. Still, FOVE operators can be applied under restricted conditions, often forcing one to resort to propositional inference. This paper aims to extend the applicability of FOVE by providing two new model conversion operators: the first and the primary is joint formula conversion and the second is just-different counting conversion. These new operations allow efficient inference methods to be applied directly on relational models, where no existing efficient method could be applied hitherto. In addition, aided by these capabilities, we show how to adapt FOVE to provide exact solutions to Maximum Expected Utility (MEU) queries over relational models for decision under uncertainty. Experimental evaluations show our algorithms to provide significant speedup over the alternatives.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
