Exact Reasoning Under Uncertainty

TL;DR

This paper advocates for the use of probabilistic models and influence diagrams in expert systems for decision making under uncertainty, demonstrating their effectiveness through a medical decision support system.

Contribution

It introduces the preference for probabilistic representations over fuzzy or Dempster-Shafer theories and discusses influence diagrams and decision analysis methodology.

Findings

Probabilistic models outperform fuzzy and Dempster-Shafer approaches.

Influence diagrams facilitate effective decision analysis.

RACHEL system demonstrates practical application in medical decision support.

Abstract

This paper focuses on designing expert systems to support decision making in complex, uncertain environments. In this context, our research indicates that strictly probabilistic representations, which enable the use of decision-theoretic reasoning, are highly preferable to recently proposed alternatives (e.g., fuzzy set theory and Dempster-Shafer theory). Furthermore, we discuss the language of influence diagrams and a corresponding methodology -decision analysis -- that allows decision theory to be used effectively and efficiently as a decision-making aid. Finally, we use RACHEL, a system that helps infertile couples select medical treatments, to illustrate the methodology of decision analysis as basis for expert decision systems.