We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of predicates. show how existing inference and techniques the underlying ProbLog can be adapted for new language. theoretically experimentally demonstrate DeepProbLog supports (i) both symbolic subsymbolic representations inference, (ii) program induction, (iii) (logic) programmi...

Tractable subsets of first-order logic are a central topic in AI research. Several these formalisms have been used as the basis for probabilistic languages. However, intractable, losing original motivation. Here we propose first non-trivially tractable language. It is subset Markov logic, and uses class part hierarchies to control complexity. We call it TML (Tractable Logic). show that knowledg...