Setting up Large-Scale Qualitative Models

A qualitative physics which captures the depth and breadth of an engineer's knowledge will be orders of magnitude larger than the models of today's qualitative physics. To build and use such models effectively requires explicit modeling assumptions to manage complexity. This, in turn, gives rise to the problem of selecting the right qualitative model for some purpose. This paper addresses these issues by describing a set of conventions for modeling assumptions. Simplifying assumptions decompose a domain into different grain sizes and perspectives which may be reasoned about separately. Operating assumptions reduce the complexity of qualitative simulation by focusing on particular behaviors of interest. We show how these assumptions can be directly represented in Qualitative Process theory, using a multi-grain, multi-slice model of a Navy propulsion plant for illustration. Importantly, we show that model selection can often be performed automatically via partial instantiation. We illustrate this technique with a simple explanation generation program that uses the propulsion plant model to answer questions about physical and functional characteristics of its operation.