Heuristics for Inductive Learning

Abstract

A number of heuristics have been developed which greatly reduce the search space a learning program must consider in its attempt to construct hypotheses about why a failure occurred. These heuristics have been implemented in the HANDICAPPER system [Salzberg 1983, Atk inson & Salzberg 1984], in which they si gnif icantly improved predicti ve ability while demonstrating a remarkable learning curve. The rationalization process has been developed as a verification system for the hypotheses suggested by the heuristics. Rationalization uses the causal knowledge of the system to ascertain whether or not a hypothesis is reasonable . If the hypothesis is not supported by causal knowledge, it is discarded and another hypothesis must be generated by the heuristics. The resulting learning system , by integrating causal knowledge with heuristic search, has quickly gone from essentially random predictive accuracy to a system which consistently outperforms the experts at predicting events in its problem domain .