On Test Selection Strategies for Belief Networks

Decision making under uncertainty typically requires an iterative process of information acquisition. At each stage, the decision maker chooses the next best test (or tests) to perform, and re-evaluates the possible decisions. Value-of-information analyses provide a formal strategy for selecting the next test(s). However, the complete decision-theoretic approach is impractical and researchers have sought approximations. In this paper, we present strategies for both myopic and limited non-myopic (working with known test groups) test selection in the context of belief networks. We focus primarily on utility-free test selection strategies. However, the methods have immediate application to the decision-theoretic framework.