Evolutionary learning of probabilistic logic programs

Learning a logic program that effectively describes input data has been a long-standing goal in Artificial Intelligence, particularly within the field of Inductive Logic Programming. Learning it is even more challenging when information is uncertain, due to the inherent complexity of probabilistic reasoning. In this paper, we propose an approach based on an evolutionary algorithm to learn probabilistic logic programs. Our empirical evaluation shows that the proposed method outperforms existing tools in terms of log-likelihood, AUCROC, and AUCPR, while also providing more compact and interpretable theories.