Foundations of Inductive Logic Programming

Ronald De Wolf

ISBN 10: 3540629270 / ISBN 13: 9783540629276

Editore: Springer Berlin Heidelberg Apr 1997, 1997

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Neuware -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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 428 pp. Englisch.

Codice articolo 9783540629276

Dati bibliografici

• Titolo: Foundations of Inductive Logic Programming
• Editore: Springer Berlin Heidelberg Apr 1997
• Data di pubblicazione: 1997
• Lingua: Inglese
• ISBN 10: 3540629270
• ISBN 13: 9783540629276
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 24 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 24 millimetri di profondità
• Peso: 645 grammi

Descrizione articolo

Riassunto

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.

Contenuti

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.