DeepStochLog: Neural Stochastic Logic Programming

Recent advances in neural-symbolic learning, such as Deep- ProbLog, extend probabilistic logic programs with neural predicates. Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard. We propose Deep- StochLog, an alternative neural-symbolic framework based on stochastic definite clause grammars, a kind of stochastic logic program. More specifically, we introduce neural grammar rules into stochastic definite clause grammars to create a framework that can be trained end-to-end. We show that inference and learning in neural stochastic logic programming scale much better than for neural probabilistic logic programs. Furthermore, the experimental evaluation shows that DeepStochLog achieves state-of-the-art results on challenging neural-symbolic learning tasks.

Authors

• Thomas Winters
• Giuseppe Marra KU Leuven
• Robin Manhaeve
• Luc De Raedt KU Leuven Örebro University

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