Foundations of Inductive Logic Programming

By: Shan-Hwei Nienhuys-Cheng, Ronald de Wolf

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Paperback | 18 April 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.

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About the Book
Logic	p. 1
Propositional Logic	p. 3
First-Order Logic	p. 17
Normal Forms and Herbrand Models	p. 35
Resolution	p. 55
Subsumption Theorem and Refutation Completeness	p. 75
Linear and Input Resolution	p. 93
SLD-Resolution	p. 105
SLDNF-Resolution	p. 127
Inductive Logic Programming	p. 161
What Is Inductive Logic Programming?	p. 163
The Framework for Model Inference	p. 179
Inverse Resolution	p. 197
Unfolding	p. 207
The Lattice and Cover Structure of Atoms	p. 219
The Subsumption Order	p. 243
The Implication Order	p. 265
Background Knowledge	p. 279
Refinement Operators	p. 299
PAC Learning	p. 321
Further Topics	p. 345
List of Symbols	p. 365
Bibliography	p. 369
Author Index	p. 391
Subject Index	p. 395
Table of Contents provided by Blackwell. All Rights Reserved.

Foundations of Inductive Logic Programming

At a Glance

Paperback

Shipping

How to return your order

Defective items

You Can Find This Book In

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