Artificial Intelligence
Integrating shallow and deep knowledge in the design of an on-line process monitoring system
International Journal of Man-Machine Studies - Special Issue: Cognitive Engineering in Dynamic Worlds
Integrating causal reasoning at different levels of abstraction
IEA/AIE '88 Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
International Journal of Man-Machine Studies
KARDIO: a study in deep and qualitative knowledge for expert systems
KARDIO: a study in deep and qualitative knowledge for expert systems
Readings in qualitative reasoning about physical systems
Readings in qualitative reasoning about physical systems
Hierarchical model-based diagnosis
International Journal of Man-Machine Studies
Varying levels of abstraction in qualitative modeling
Machine intelligence 12
Artificial Intelligence
Heterogeneous decomposition and inter-level coupling for combined modeling
WSC '91 Proceedings of the 23rd conference on Winter simulation
Reasoning with qualitative disease histories for diagnostic patient monitoring
Reasoning with qualitative disease histories for diagnostic patient monitoring
Model-based monitoring of dynamic systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Measuring and improving the effectiveness of representations
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Temporal abstraction in intelligent clinical data analysis: A survey
Artificial Intelligence in Medicine
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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.