Published:
May 1999
Proceedings:
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)
Volume
Issue:
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)
Track:
All Papers
Downloads:
Abstract:
The increasing amount of information to be managed in knowledge-based systems has promoted, on one hand, the exploitation of machine learning for the automated acquisition of knowledge and, on the other hand, the adoption of object-oriented representation models for easing the maintenance. In this context, adopting techniques for structuring knowledge representation in machine learning seems particularly appealing. Inductive Logic Programming (ILP) is a promising approach for the automated discovery of rules in knowledge based systems. We propose an object-oriented extension of ILP employing multi-theory logic programs as the representation language. We define a new learning problem and propose the corresponding learning algorithm. Our approach enables ILP to benefit of object-oriented domain modelling in the learning process, such as allowing structured domains to be directly mapped onto program constructs, or easing the management of large knowledge bases.
![](https://aaai.org/wp-content/uploads/2023/06/flairs99.jpg)
FLAIRS
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)
ISBN 978-1-57735-080-4
Published by The AAAI Press, Menlo Park, California.