Inductive Logic Programming: Techniques and Applications

Appeared in Volume 7/1, February 1994

Nada Lavrac and Saso Dzeroski

The book is an introduction to inductive logic programming (ILP), a research area at the intersection of inductive machine learning and LP. This field aims at a formal framework and practical algorithms for inductively learning relational descriptions in the form of logic programs.

The book consists of four parts. Part I is an introduction to the field of ILP. Part II describes in detail several empirical ILP techniques and their implementations. Part III presents the techniques for handling imperfect data in ILP, and Part IV gives an overview of several ILP applications.

The book serves two main purposes. Firstly, it can be used as a course book on ILP since it provides an easy-to-read introduction to ILP (Chapters 1-3), an overview of empirical ILP systems (Chapter 4), discusses ILP as search of refinement graphs (Chapter 7), analyses the sources of imperfect/noisy data and the mechanisms for handling noise (Chapter 8) and gives an overview of several interesting applications of ILP (Chapter 14). Secondly, the book is a guide/reference for an in-depth study of specific empirical ILP techniques, i.e., using attribute-value learners in an ILP framework and specialization techniques based on FOIL (Chapters 5-6,9-10) and their applications in medicine, mesh design and learning of qualitative models (Chapters 11-13).

1994; 20+294 pages; Hardcover US$53.95
ISBN 0-13-457870-8
Ellis Horwood Series in Artificial Intelligence
Ellis Horwood (Simon and Schuster)