Inductive logic programming is the subfield of machine learning that uses first-order logic to represent hypotheses and data. Because first-order logic is expressive and declarative, inductive logic programming specifically targets problems involving structured data and background knowledge. Inductive logic programming tackles a wide variety of problems in machine learning, including classification, regression, clustering, and reinforcement learning, often using “upgrades” of existing propositional machine learning systems. It relies on logic for knowledge representation and reasoning purposes. Notions of coverage, generality, and operators for traversing the space of hypotheses are grounded in logic, see also logic of generality. Inductive logic programming systems have been applied to important applications in bio- and chemo-informatics, natural language processing,...