  Inductive logic programming (ILP) is a form of logic-based machine learning.
The goal is to induce a hypothesis (a logic program) that generalises given
training examples. As ILP turns 30, we review the last decade of research. We
focus on (i) new meta-level search methods, (ii) techniques for learning
recursive programs, (iii) new approaches for predicate invention, and (iv) the
use of different technologies. We conclude by discussing current limitations of
ILP and directions for future research.
