Abstract
Conversational case-based reasoning (CBR) systems, which
incrementally extract a query description through a user-directed
conversation, are advertised for their ease of use. However,
designing large case libraries that have good performance (i.e.\
precision and querying efficiency) is difficult. CBR vendors
provide guidelines for designing these libraries manually, but
the guidelines are difficult to apply. We describe an automated
inductive approach that revises conversational case libraries to
increase their conformance with design guidelines. Revision
increased performance on three conversational case libraries.
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