Deep learning for probabilistic logic programming

Due to its expressiveness and intuitiveness, Probabilistic logic programming (PLP) is a useful tool for reasoning in relational domains with uncertainty. However, both inference and learning are expensive tasks. In this paper we present various approaches for speeding up learning. We first consider a restriction of PLP called Liftable PLP (LPLP) in which clauses in the program share the same predicate (the target). Then we extend this restriction in Hierarchical PLP (HPLP) where predicates and clauses are hierarchically organized and can be translated into Deep Neural Networks or Arithmetic Circuits. For LPLP, we propose two parameter learning algorithms, Expectation Maximization (EM) and Limited memory BFGS (LBFGS), and a discriminative structure learning. We also propose and implement an algorithm, called Parameter learning for HIerarchical probabilistic Logic program (PHIL) 3 that learns the parameter of HPLP using EM and gradient method.