Reasoning with multiple abstraction models
Knowledge Systems Laboratory
701 Welch Road,
Palo Alto, CA 94304
Originally presented at Fourth International Workshop on Qualitative Physics, 1990.
An expanded version was ublished in Recent Advances in Qualitative Physics, Faltings, B. and Struss, P. eds., MIT Press, 1992.
The problem of complexity has kept qualitative physics techniques from being applied to large real-world systems. Use of a hierarchy of abstract models is crucial for managing complexity. Several researchers have proposed ways to use an abstraction hierarchy of models to control the complexity of qualitative simulation [Falkenhainer & Forbus 88, Kuipers 87]. All the approaches proposed require models at pre-defined abstraction levels. Furthermore, the precise relations between different models are not explicitly defined, which makes it difficult to relate the conclusions drawn from different models to generate one coherent description of the behavior of the system as a whole. In this paper, we describe a scheme for generating models at abstraction levels appropriate for a given problem without requiring pre-defined set of abstract models. We also propose means for integrating behaviors produced from different abstraction models into one coherent description.