Probabilistic Logic Programming (PLP 2017)

Probabilistic Logic Programming (PLP) has come to the fore in the last decades as one of the most promising ways to model and reason on many different domains, including for example bioinformatics, semantic web, robotics, and computer vision. Such domains have in common the fact that information may be incomplete and/or uncertain, requiring approaches able to cope with such uncertainty. Developments in PLP include new languages that combine logic programming with probability theory and algorithms that operate on programs in these formalisms. Moreover, active fields such as Inductive Logic Programming and Statistical Relational Learning heavily use PLP. Following the Fourth Workshop on Probabilistic Logic Programming (PLP 2017), which was held on September 7, 2017, in Orléans, France and co-located with the 27th International Conference on Inductive Logic Programming (ILP 2017), this special issue is devoted to all aspects of probabilistic logic programming, including theoretical work, system implementations and applications. This special issue contains four high-quality papers which were accepted for publication