ABSTRACT. Computing value of information (VOI) is a crucial task in various aspects of decision-making under uncertainty, such as in meta-reasoning for search; in selecting measurements to make, prior to choosing a course of action; and in managing the exploration vs. exploitation tradeoff. Since such applications typically require numerous VOI computations during a single run, it is essential that VOI be computed efficiently. We examine the issue of anytime estimation of VOI, as frequently it suffices to get a crude estimate of the VOI, thus saving considerable computational resources. As a case study, we examine VOI estimation in the measurement selection problem. Empirical evaluation of the proposed scheme in this domain shows that computational resources can indeed be significantly reduced, at little cost in expected rewards achieved in the overall decision problem.
Taking a broader view, we note that computing the VOI is an inherent part of decisionmaking under uncertainty and therefore is commonly found in mechanisms designed for meta-reasoning for search, in selecting measurements to make prior to choosing a course of action, and in managing the exploration vs. exploitation tradeoff REF .
Rational Value of Information Estimation for Measurement Selection
ArXiv
ArXiv
Computer Science