Title: Differentiable Logic Programming for Probabilistic Reasoning. Abstract: This paper studies inductive logic programming for probabilistic reasoning. The key problems, i.e. learning rule structures and learning rule weights, have been extensively studied with traditional discrete searching methods as well as recent neural-based approaches. In this paper, we present a new approach called Differentiable Logic Programming (DLP), which provides a flexible framework for learning first-order logical rules for reasoning. We propose a continuous version of optimization problem for learning high-quality rules as a proxy and generalize rule learning and forward chaining algorithms in a differentiable manner, which enables us to efficiently learn rule structures and weights via gradient-based methods. Theoretical analysis and empirical results show effectiveness of our approach.