Intermediate depth representations

The limitations of shallow representations have in part driven AI researchers to focus on deeper representations of knowledge. While deep representations solve some problems, they come at a computational cost. This paper focuses on the computational and representational advantages that may exist in using representations whose depth is intermediate between shallow and deep. The Roschian notion of basic level categories is used to help develop the notion of the cognitively most economic representation. For medical diagnostic systems that reason about time varying aspects of disease, it is proposed that qualitative disease histories are a good intermediate representation, lying between shallow disease patterns and deeper qualitative models. Since no single representation will provide complete coverage of a problem domain, this paper further considers how one could construct, in a principled way, a reasoning system that uses multiple representations. Measures of intra- and inter-representational adequacy are proposed to define the optimal level of such a knowledge base for a given problem. These measures define the trade-offs that occur when using a particular representational level, and the conditions under which a reasoner can decide to switch representations. As an example, the formal relationships between histories and the qualitative models that produce them are shown to define conditions that can be used by a reasoning system to switch from histories to deeper models.