Decision Under Uncertainty in Diagnosis

TL;DR

This paper explores how to incorporate uncertainty into diagnostic reasoning using an extended set covering model, emphasizing the importance of a strong underlying model and support tools for effective diagnosis.

Contribution

It introduces an extension of the set covering model for diagnosis that accounts for uncertainty, bridging deep and compiled knowledge approaches.

Findings

Extended set covering model effectively handles uncertainty in diagnosis.

Highlights the need for integrated support tools and strong models.

Bridges the gap between deep and compiled knowledge diagnosis.

Abstract

This paper describes the incorporation of uncertainty in diagnostic reasoning based on the set covering model of Reggia et. al. extended to what in the Artificial Intelligence dichotomy between deep and compiled (shallow, surface) knowledge based diagnosis may be viewed as the generic form at the compiled end of the spectrum. A major undercurrent in this is advocating the need for a strong underlying model and an integrated set of support tools for carrying such a model in order to deal with uncertainty.