David B. Leake Explanation-based learning (EBL) is a powerful
method for category formation. However, EBL systems are only
effective if they start with good explanations. The problem of
evaluating candidate explanations has received little attention:
Current research usually assumes that a single explanation will
be available for any situation, and that this explanation will
be appropriate. In the real world many explanations can be
generated for a given anomaly, only some of which are
reasonable. Thus it is crucial to be able to distinguish between
good and bad explanations. In people, the criteria for
evaluating explanations are dynamic: they reflect context, the
explainer’s current knowledge, and his needs for specific
information. I present a theory of how these factors affect
evaluation of explanations, and describe its implementation in
ACCEPTER, a program to evaluate explanations for anomalies
detected during story understanding.