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DeepProbLog: Neural Probabilistic Logic Programming

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arxiv 1805.10872 v2 pith:DPGZJVJV submitted 2018-05-28 cs.AI

Robin Manhaeve , Sebastijan Dumančić , Angelika Kimmig , Thomas Demeester , Luc De Raedt This is my paper · ORCID
classification cs.AI
keywords deepprobloglearninglogicneuralprobabilisticprogrammingdeepexamples
verification ladder T0 review T1 audit T2 compute T3 formal
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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 both symbolic and subsymbolic representations and inference, 1) program induction, 2) probabilistic (logic) programming, and 3) (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.

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Cited by 6 Pith papers

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