Abstract

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. 9.1 Introduction Graphical belief network researchers have developed powerful algorithms to propagate the effects of any piece of information to all the variables in the model in a manner analogous to forward chaining in rule-based expert systems (see, for example, Daw...

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