  Computational mechanisms for uncertainty management must support interactive
and incremental problem formulation, inference, hypothesis testing, and
decision making. However, most current uncertainty inference systems
concentrate primarily on inference, and provide no support for the larger
issues. We present a computational approach to uncertainty management which
provides direct support for the dynamic, incremental aspect of this task, while
at the same time permitting direct representation of the structure of
evidential relationships. At the same time, we show that this approach responds
to the modularity concerns of Heckerman and Horvitz [Heck87]. This paper
emphasizes examples of the capabilities of this approach. Another paper
[D'Am89] details the representations and algorithms involved.
