Knowledge Acquisition with a Knowledge-Intensive Machine Learning
System

Clifford A. Brunk and Michael J. Pazzani
Department of Information and Computer Science
University of California
Irvine, CA 92717
(brunk@ics.uci.edu)
(pazzani@ics.uci.edu)

Abstract

In this paper, we investigate the integration of knowledge acquisition
and machine learning techniques.  We argue that existing machine
learning techniques can be made more useful as knowledge acquisition
tools by allowing greater control over and interaction with the
learning process.  We describe a number of extensions to FOCL (a
multistrategy Horn-clause learning program) that have greatly enhanced
its power as a knowledge acquisition tool, paying particular attention
to the usefulness of maintaining a connection between a rule and the
set of examples that are explained by the rule.  The objective of this
research is to make the modification of a domain theory analogous to
the use of a spread sheet.  A prototype knowledge acquisition tool,
FOCL-1-2-3, has been constructed in order to evaluate the strengths
and weaknesses of this approach.