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.

中文翻译：

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概率逻辑编程的 MAP 推理

在概率逻辑编程 (PLP) 中，最常研究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：Maximum-A-Posteriori (MAP) 推理任务，它确定给定其他变量证据的随机变量子集的最可能值，以及最可能的解释 (MPE) 任务，MAP 的实例，其中查询变量是证据变量的补充。我们提出了一种新颖的算法，包含在 PITA 推理器中，它通过将每个问题表示为二进制决策图并在其上应用动态规划过程来解决这些任务。我们将我们的算法与 ProbLog 版本进行比较，该版本承认带注释的析取并可以执行 MAP 和 MPE 推理。