Pinpointing good hypotheses with heuristics
Artificial intelligence and statistics
A Computer Model of Skill Acquisition
A Computer Model of Skill Acquisition
Learning by knowledge sharing in autonomous intelligent systems
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Heuristic rule induction for decision making in near-deterministic domains
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
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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, Atkinson & Salzberg 1984], in which they significantly improved predictive 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 w i th 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.