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

Special Issue of the 4th Workshop on Probabilistic Logic Programming

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

Proceedings of the 4th International Workshop on Probabilistic Logic Programming (PLP)

These are the proceedings of the Third Workshop on Probabilistic Logic Programming (PLP 2017), which was held on September 7, 2016 in Orleans, France. The workshop was co-located with the 27th International Conference on Inductive Logic Programming (ILP 2017). Five papers were submitted to the workshop of which four was accepted to be presented during the workshop. Each submission was reviewed by at least three members of the program committee. In addition, the workshop also had two invited talks: “Inference for Probabilistic Logic Programming with Continuous Distributions“ by Arjen Hommersom from Open University of the Netherlands, and ”PRISM Revisited: Declarative Implementation of a Probabilistic Programming Language Using Delimited Control” by Samer Abdallah of Jukedeck Ltd. This workshop is the fourth edition of the PLP series. The two first editions were part of the ICLP conference: the first one in 2014 in Vienna, Austria, and the second one in Cork, Ireland. The third edition was co-located with the ILP conference in London, 2017. More information about the current edition, the previous edition, and future editions can be found at the following website: http://stoics.org.uk/plp/. We would like to thank all authors who submitted papers, and the program committee members for their efforts. We are especially grateful to Nicos Angelopoulos for his continued support for the workshop series, and his help with the organisation of the 2017 edition