Inductive Logic Programming

This book is an introduction to inductive logic programming (ILP), a research field at the intersection of machine learning and logic programming, which aims at a formal framework as well as practical algorithms for inductively learning relational descriptions in the form of logic programs. The book extensively covers empirical inductive logic programming, one of the two major subfields of ILP, which has already shown its application potential in the following areas: knowledge acquisition, inductive program synthesis, inductive data engineering, and knowledge discovery in databases.

The book provides the reader with an in-depth understanding of empirical ILP techniques and applications. It is divided into four parts. Part I is an introduction to the field of ILP. Part II describes in detail empirical ILP techniques and systems. Part III presents the techniques of handling imperfect data in ILP, whereas Part IV gives an overview of empirical ILP applications.

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