Explanation-based learning of diagnostic heuristics: a comparison of learning from success and failure

The author compares strategies of learning from failures to learning from successes in the context of a generate-and-test problem solver. One result is fairly straightforward: failure-driven learning creates rules which distinguish between failures. This is demonstrated by the fact that the number of hypotheses decreases after learning. A more subtle result is that the performance of the system, measured in terms of logical inferences, decreased with failure-driven learning more than it did with two variants of success driven learning. Diagnosis results are presented for ACES designed to process telemetry data from a satellite and isolate the cause of problems with the attitude control system.<<ETX>>