Rational Value of Information Estimation for Measurement Selection

TL;DR

This paper explores efficient, anytime estimation methods for value of information in measurement selection, demonstrating significant resource savings with minimal impact on decision quality.

Contribution

It introduces an anytime VOI estimation scheme tailored for measurement selection, improving computational efficiency in decision-making under uncertainty.

Findings

Significant reduction in computational resources needed for VOI estimation.

Minimal loss in expected rewards when using the proposed estimation scheme.

Effective application demonstrated in measurement selection scenarios.

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.