Foundations of Inductive Logic Programming

by Shan-Hwei Nienhuys-Cheng, Ronald de Wolf

Overview

Inductive Logic Programming is a young and rapidly growing field combining machine learning and logic programming. This self-contained tutorial is the first theoretical introduction to ILP; it provides the reader with a rigorous and sufficiently broad basis for future research in the area.

In the first part, a thorough treatment of first-order logic, resolution-based theorem proving, and logic programming is given. The second part introduces the main concepts of ILP and systematically develops the most important results on model inference, inverse resolution, unfolding, refinement operators, least generalizations, and ways to deal with background knowledge. Furthermore, the authors give an overview of PAC learning results in ILP and of some of the most relevant implemented systems.

Product Details

ISBN-13:	9783540629276
Publisher:	Springer Berlin Heidelberg
Publication date:	05/29/1997
Series:	Lecture Notes in Computer Science , #1228
Edition description:	1997
Pages:	410
Product dimensions:	6.10(w) x 9.25(h) x 0.36(d)

Table of Contents

Propositional logic.- First-order logic.- Normal forms and Herbrand models.- Resolution.- Subsumption theorem and refutation completeness.- Linear and input resolution.- SLD-resolution.- SLDNF-resolution.- What is inductive logic programming?.- The framework for model inference.- Inverse resolution.- Unfolding.- The lattice and cover structure of atoms.- The subsumption order.- The implication order.- Background knowledge.- Refinement operators.- PAC learning.- Further topics.