MAP Inference for Probabilistic Logic Programming
This work addresses inference challenges in probabilistic logic programming, offering improved performance for researchers and practitioners in AI and logic-based systems, though it appears incremental as it builds on existing tasks and methods.
The paper tackles the Maximum-A-Posteriori (MAP) and Most Probable Explanation (MPE) inference tasks in Probabilistic Logic Programming by introducing a novel algorithm using Binary Decision Diagrams and dynamic programming, showing that PITA outperforms ProbLog in many cases on synthetic datasets.
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.