Inductive logic programming at 30
Andrew Cropper, Sebastijan Dumančić, Richard Evans, Stephen Muggleton
Abstract
Abstract 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 and background knowledge. 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.
Topics & Concepts
Inductive logic programmingLogic programmingInductive programmingComputer scienceLogic programArtificial intelligenceProgramming languageFocus (optics)Predicate logicStatistical relational learningMachine learningProgramming paradigmDescription logicRelational databaseOpticsDatabasePhysicsLogic, Reasoning, and KnowledgeBayesian Modeling and Causal InferenceSoftware Engineering Research