Inductive logic programming at 30
This is an incremental review paper for researchers in ILP and logic-based AI, summarizing recent developments and identifying future directions.
The paper reviews the last decade of research in inductive logic programming (ILP), a logic-based machine learning approach, focusing on advancements in search methods, recursive program learning, predicate invention, and technology use, without presenting new experimental results.
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.