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Foundations of Inductive Logic Programming / by Shan-Hwei Nienhuys-Cheng, Ronald de Wolf.

SpringerLink Books Lecture Notes In Computer Science (LNCS) (1997-2024) Available online

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Format:
Book
Author/Creator:
Nienhuys-Cheng, Shan-Hwei, author.
Wolf, Ronald de, author.
Contributor:
SpringerLink (Online service)
Series:
Computer Science (Springer-11645)
Lecture notes in computer science. Lecture notes in artificial intelligence ; 1228.
Lecture Notes in Artificial Intelligence ; 1228
Language:
English
Subjects (All):
Software engineering.
Artificial intelligence.
Logic, Symbolic and mathematical.
Computer programming.
Software Engineering/Programming and Operating Systems.
Artificial Intelligence.
Mathematical Logic and Formal Languages.
Programming Techniques.
Local Subjects:
Software Engineering/Programming and Operating Systems.
Artificial Intelligence.
Mathematical Logic and Formal Languages.
Programming Techniques.
Physical Description:
1 online resource (XVIII, 410 pages).
Edition:
First edition 1997.
Contained In:
Springer eBooks
Place of Publication:
Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1997.
System Details:
text file PDF
Summary:
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.
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.
Other Format:
Printed edition:
ISBN:
978-3-540-69049-8
9783540690498
Access Restriction:
Restricted for use by site license.

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