Process, Structure, and Modularity in Reasoning with Uncertainty

TL;DR

This paper introduces a computational approach for uncertainty management that supports dynamic, incremental problem solving and explicitly represents evidential structures, addressing limitations of existing inference systems.

Contribution

It presents a novel method for uncertainty management that enables interactive, incremental reasoning with explicit structural representation, enhancing modularity and flexibility.

Findings

Supports dynamic, incremental problem formulation

Allows direct representation of evidential relationships

Addresses modularity concerns in uncertainty reasoning

Abstract

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.