Neural probabilistic logic programming in DeepProbLog

We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the new language. We theoretically and experimentally demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv)(deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

DeepProbLog is a neural probabilistic logic programming language that supports symbolic and subsymbolic representations and inference, program induction, probabilistic programming, and learning from examples. It integrates general-purpose neural networks and expressive probabilistic-logical modeling and reasoning, allowing end-to-end training based on examples.