Foundations of Inductive Logic Programming
Nienhuys-Cheng, Shan-Hwei.
creator
author.
Wolf, Roland de.
author.
SpringerLink (Online service)
text
gw
1997
monographic
eng
access
XVIII, 410 p. online resource.
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.
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.
by Shan-Hwei Nienhuys-Cheng, Roland de Wolf.
Computer science
Software engineering
Computer programming
Mathematical logic
Artificial intelligence
Computer Science
Software Engineering/Programming and Operating Systems
Artificial Intelligence (incl. Robotics)
Mathematical Logic and Formal Languages
Programming Techniques
QA76.758
005.1
Springer eBooks
Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, 1228
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http://dx.doi.org/10.1007/3-540-62927-0
http://dx.doi.org/10.1007/3-540-62927-0
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