Explanation-based indexing of cases
Ralph Barletta, William R. Mark · 1988
Proper indexing of cases is critically important to the functioning of a case-based reasoner. In real domains such as fault recovery, a body of do-main knowledge exists that can be captured and brought to bear on the indexing problem-even though the knowledge is incomplete. Modified explanation-based learning techniques allow the use of the incomplete domain theory to justify the actions of a case with respect to the facts known when the case was originally executed. Demonstrably relevant facts are generalized to form primary indices for the case. Inconsisten-cies between the domain theory and the actual case can also be used to determine facts that are