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.

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Bibliographic Information

Book Title: Foundations of Inductive Logic Programming
Authors: Shan-Hwei Nienhuys-Cheng, Roland Wolf
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/3-540-62927-0
Publisher: Springer Berlin, Heidelberg
eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 1997
Softcover ISBN: 978-3-540-62927-6Published: 18 April 1997
eBook ISBN: 978-3-540-69049-8Published: 01 July 2005
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XVIII, 410
Topics: Software Engineering/Programming and Operating Systems, Artificial Intelligence, Mathematical Logic and Formal Languages, Programming Techniques

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Table of contents (19 chapters)

Front Matter

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