Abstract
This paper is aimed at showing the benefits obtained by explicitly introducing a priori control knowledge into the inductive process. The starting point is Michalski's Induce system, which has been modified and augmented. Although the basic philosophy has been changed as little as possible, Induce has been radically modified from the algorithmic point of view, resulting in the new learning system Rigel. The main ideas taken from Induce are the sequential learning of descriptions of each concept against all the others, the Covering algorithm, the Star definition, and the VL2 representation language. The modifications consist of a new way of computing the Star, the use of a separate body of heuristic knowledge to strongly direct the search, the implementation of a larger subset of the VL2 language, a reasoned way of selecting the seed, and the use of rules to evaluate the worthiness of the inductive assertions. The effectiveness of Rigel has been tested both on artificial and on real-world case studies.
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