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.

翻译：引入逻辑编程(ILP)是一种基于逻辑的机器学习形式,目的是引出一种假设(逻辑程序),概括一些培训实例。当ILP满30岁时,我们审查了过去十年的研究情况。我们侧重于(一) 新的元级搜索方法,(二) 学习循环程序的技术,(三) 上游发明的新办法,(四) 不同技术的使用。我们最后通过讨论目前ILP的局限性和今后研究的方向。