A fundamental issue in case-based reasoning is similarity assessment: determining similarities and diierences between new and retrieved cases. Many methods have been developed for comparing input case descriptions to the cases already in memory. However, the success of such methods depends on the input case description being suuciently complete to reeect the important features of the new situation, which is not assured. In case-based explanation of anomalous events during story understanding, the anomaly arises because the current situation is incompletely understood ; consequently, similarity assessment based on matches between known current features and old cases is likely to fail because of gaps in the current case's description. Our solution to the problem of gaps in a new case's description is an approach that we call constructive similarity assessment. Constructive similarity assessment treats similarity assessment not as a simple comparison between xed new and old cases, but as a process for deciding which types of features should be investigated in the new situation and, if the features are borne out by other knowledge, added to the description of the current case. Constructive similarity assessment does not merely compare new cases to old: using prior cases as its guide, it dynamically carves augmented descriptions of new cases out of memory.
[1]
Kevin D. Ashley,et al.
Compare and Contrast: A Test of Expertise
,
1987,
AAAI.
[2]
R. Bareiss.
Exemplar-Based Knowledge Acquisition: A Unified Approach to Concept Representation, Classification, and Learning
,
1990
.
[3]
Karl Branting,et al.
Rules and Precedents as Complementary Warrants
,
1991,
AAAI.
[4]
Kristian J. Hammond,et al.
Case-Based Planning: Viewing Planning as a Memory Task
,
1989
.
[5]
Charles J. Rieger,et al.
CONCEPTUAL MEMORY AND INFERENCE
,
1975
.
[6]
Roger C. Schank,et al.
Creativity and Learning in a Case-Based Explainer
,
1989,
Artif. Intell..
[7]
J J Flink.
THE FAMILY CAR
,
1987
.
[8]
Janet L. Kolodner,et al.
Extending Problem Solver Capabilities Through Case-Based Inference
,
1987
.