Some Limitations of Feature-Based Recognition
in Case-Based Design

Thomas R. Hinriche

The Institute for the Learning Sciences
Northwestern University
Evanston, IL 60201


Abstract. A crucial part of Case-Based Reasoning is retrieving cases
that are similar or otherwise relevant to the problem at hand. Traditionally, 
this has been formulated as a problem of indexing and accessing
cases based on sets of predictive features. More generally, however, we
can think of retrieval as a problem of recognition. In this light, several
limitations of the feature-based approach become apparent. What constitutes 
a feature? What makes a feature predictive? And how is retrieval
possible when the structure of an input is predictive, but its components
are not?
	This paper presents an analysis of some of the limitations of feature-based 
recognition and describes a process that integrates structural recognition 
with retrieval. This structural recognition algorithm is designed
to augment the retrieval capabilities of case-based reasoners by facilitating 
the recognition of functional design clichs, natural laws, and sub
problems for which individual features may not be predictive.
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