This paper focusses on one microcosm of machine learning and on a family of
learning systems that have been used to build knowledge-based systems of a simple
kind. Section 2 outlines the features of this family and introduces its members. All
these systems address the same task of inducing decision trees from examples. After
a more complete specification of this task, one system (ID3) is described in detail in
Section 4. Sections 5 and 6 present extensions to ID3 that enable it to cope with noisy
and incomplete information. A review of a central facet of the induction algorithm
reveals possible improvements that are set out in Section 7. The paper concludes with
two novel initiatives that give some idea of the directions in which the family may
grow.