LAUSR

We are pleased to present this special issue of Machine Learning on inductive logic programming (ILP). The papers contained in this volume are extended and thoroughly revised versions of shorter papers which were first presented at the 26th international conference on inductive logic programming (ILP’16). ILP 2016 was held in London, during September 4–6 2016, at the Warren House ConferenceCentre. Since its first edition in 1991, the annual ILP conference has served as the premier international forum for learning from structured relational data. Originally focusing on the induction of logic programs, over the years it has expanded its research horizon significantly and welcomed contributions on all aspects of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections with statistical learning and other probabilistic approaches. Theoretical advances in all these areas have also been accompanied by challenging applications of these techniques to important problems in fields like bioinformatics, medicine, and text mining. ILP’16 received 35 submissions in total: 10 long papers, 19 short papers, and 6 alreadypublished papers. Of these 35 submissions only 4 out of the unpublished papers have made it into this special issue. Our first paper Meta-Interpretive Learning from noisy images by Muggleton et al. provides a novel approach to image classification called Logical Vision. An approach to ILP called Meta-Interpretative Learning is used to learn an accurate classifier based on as few as a one example, due to the use of rich (learnable) background knowledge. The second paper Ultra-strong machine learning: Comprehensibility of programs learned with ILP, also by Muggleton et al, focuses on what has long been a key ‘selling point’ of ILP: the comprehensibility (for humans) of what is learned. A key issue addressed by this