We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments 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.

This work extends the ProbLog language and uses the distribution of grounded facts estimated by the ProbLog to train neural networks, which is represented as neural predicates in the ProbLog. Meanwhile, the DeepProbLog framework is able to learn ProbLog parameters and deep neural networks at the same time. The experimental results show that the DeepProbLog can perform joint probabilistic logical reasoning and neural network inference on some simple tasks. Combining perception and symbolic reasoning is an important challenge for AI and machine learning. Different to most of the existing works, this work does not make one side subsumes the other (e.g.

We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments 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. Papers published at the Neural Information Processing Systems Conference.