Abstract
Inductive Logic Programming (ILP, for short) is machine learning using logic programs and is useful for knowledge discovery from structured data. In this survey we explain foundations of ILP, based on computational learning theory and comparing it with parameter estimation in statistics. In particular we explain details of refinement operations which are defined with deductive inference rules, with showing their role in machine learning procedures. We also introduce some recent topics in ILP research.
