Process, structure and modularity in reasoning with uncertainty

A computational approach to uncertainty management requires support for interactive and incremental problem formulation, inference, hypothesis testing, and decision making. Most current uncertainty inference systems concentrate primarily on inference, and provide no sup­ port 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 Beckerman and Horvitz [Heckerman & Horvitz, 1987]. This paper emphasizes examples of the capabilities of this approach. Another paper [D'Ambrosio, 1988] details the representations and algorithms