Foundations Of Inductive Logic Programming by Shan-Hwei Nienhuys-ChengFoundations Of Inductive Logic Programming by Shan-Hwei Nienhuys-Cheng

Foundations Of Inductive Logic Programming

byShan-Hwei Nienhuys-Cheng, Ronald De Wolf

Paperback | April 18, 1997

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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.
Title:Foundations Of Inductive Logic ProgrammingFormat:PaperbackDimensions:428 pagesPublished:April 18, 1997Publisher:Springer Berlin Heidelberg

The following ISBNs are associated with this title:

ISBN - 10:3540629270

ISBN - 13:9783540629276

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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.