Although Inductive Logic Programming (ILP) is generally thought of as a research area
at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that
most of the research in ILP has in fact come from machine learning, particularly in the evolution of
inductive reasoning from pattern recognition, through initial approaches to symbolic machine
learning, to recent techniques for learning relational concepts. In this book they provide an
extended, up-to-date survey of ILP, emphasizing methods and systems suitable for software
engineering applications, including inductive program development, testing, and
maintenance.Inductive Logic Programming includes a definition of the basic ILP problem and its
variations (incremental, with queries, for multiple predicates and predicate invention
capabilities), a description of bottom-up operators and techniques (such as least general
generalization, inverse resolution, and inverse implication), an analysis of top-down methods
(mainly MIS and FOIL-like systems), and a survey of methods and languages for specifying inductive
bias.Logic Programming series