  In almost all situation assessment problems, it is useful to dynamically
contract and expand the states under consideration as assessment proceeds.
Contraction is most often used to combine similar events or low probability
events together in order to reduce computation. Expansion is most often used to
make distinctions of interest which have significant probability in order to
improve the quality of the assessment. Although other uncertainty calculi,
notably Dempster-Shafer [Shafer, 1976], have addressed these operations, there
has not yet been any approach of refining and coarsening state spaces for the
Bayesian Network technology. This paper presents two operations for refining
and coarsening the state space in Bayesian Networks. We also discuss their
practical implications for knowledge acquisition.
