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 covers empirical inductive logic programming, one of the two major subfields of ILP, which has 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.
The book is intended for knowledge engineers
concerned with the automatic synthesis of knowledge bases for expert systems, software engineers
who could profit from inductive programming tools, researchers
in system development and database methodology, interested in techniques for knowledge discovery in databases and inductive data engineering, and researchers and graduates
in artificial intelligence, machine learning, logic programming, software engineering and database methodology.