Case-based reasoning involves reasoning from {\em cases}: specific pieces
of experience, the reasoner's or another's, that can be used to solve
problems. As a result, case representation is critical: an incomplete case
representation limits the system's reasoning power. In this paper we
argue for {\em structure-based} case representations, which express
arbitrary relations among objects in a flexible way, over more limited or
inflexible methods. We motivate the distinction between these kinds of
representations with examples from information retrieval systems, CBR
systems, and computational models of human analogical reasoning.
Structure-based representations provide the benefits of greater
expressivity and economy. We give examples of these benefits from two
case-based planning systems we have developed, CaPER and CHIRON, and show
how the case matching and case acquisition costs can be reduced through
the use of massively parallel techniques.
(Also cross-referenced as UMIACS-TR-95-56
