Learning the parameters of deep probabilistic logic programs

Probabilistic logic programming (PLP) is a powerful tool for reasoning in relational domains with uncertainty. However, both inference and learning from examples are computationally expensive tasks. We consider a restriction of PLP called hierarchical PLP whose clauses and predicates are hierarchically organized forming a deep neural network or arithmetic circuit. Inference in this language is much cheaper than for general PLP languages. In this work we present an algorithm called Deep Parameter learning for HIerarchical probabilistic Logic programs (DPHIL) that learns hierarchical PLP parameters using gradient descent and back-propagation.