论文标题
概率逻辑编程的映射推断
MAP Inference for Probabilistic Logic Programming
论文作者
论文摘要
在概率逻辑编程（PLP）中，最常见的推理任务是计算给定程序的查询的边际概率。在本文中，我们考虑了PLP设置中的另外两个重要任务：最大A-posteriori（MAP）推理任务，这确定了在其他变量上给出的随机变量子集的最可能值，以及最可能的解释（MPE）任务（MPE）任务，地图的实例，查询变量是证据的互补的。我们提出了一种新颖的算法，其中包括PITA推理器中，该算法通过将每个问题表示为二进制决策图并在其上应用动态编程程序来解决这些任务。我们将算法与接受注释的析出并可以执行MAP和MPE推断的Problog版本进行比较。几个合成数据集的实验表明，在许多情况下，PITA优于problog。
In Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the marginal probability of a query given a program. In this paper, we consider two other important tasks in the PLP setting: the Maximum-A-Posteriori (MAP) inference task, which determines the most likely values for a subset of the random variables given evidence on other variables, and the Most Probable Explanation (MPE) task, the instance of MAP where the query variables are the complement of the evidence variables. We present a novel algorithm, included in the PITA reasoner, which tackles these tasks by representing each problem as a Binary Decision Diagram and applying a dynamic programming procedure on it. We compare our algorithm with the version of ProbLog that admits annotated disjunctions and can perform MAP and MPE inference. Experiments on several synthetic datasets show that PITA outperforms ProbLog in many cases.
