OGUST

This chapter presents a system, called OGUST, which learns concepts from sets of examples, and shows that for learning “good” generalizations, one must use all kinds of theorems and not only those expressed by taxonomies.

Abstract

In this chapter, we present a system, called OGUST, which learns concepts from sets of examples. Presently, most such systems use only properties of the domain expressed as taxonomies or use only a few simple theorems. First, we show that for learning "good" generalizations, we must use all kinds of theorems and not only those expressed by taxonomies. Then we explain how in OGUST, we control the use of theorems to apply only those that may improve the generalization, how we avoid the problem of loops, and how the use of theorems enables to increase the explicability of the system.