TY - BOOK
AU - Nienhuys-Cheng,Shan-Hwei
AU - Wolf,Roland de
ED - SpringerLink (Online service)
TI - Foundations of Inductive Logic Programming
T2 - Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence,
SN - 9783540690498
AV - QA76.758
U1 - 005.1 23
PY - 1997///
CY - Berlin, Heidelberg
PB - Springer Berlin Heidelberg
KW - Computer science
KW - Software engineering
KW - Computer programming
KW - Mathematical logic
KW - Artificial intelligence
KW - Computer Science
KW - Software Engineering/Programming and Operating Systems
KW - Artificial Intelligence (incl. Robotics)
KW - Mathematical Logic and Formal Languages
KW - Programming Techniques
N1 - 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
N2 - 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
UR - http://dx.doi.org/10.1007/3-540-62927-0
ER -