Semi-Myopic Sensing Plans for Value Optimization

TL;DR

This paper introduces semi-myopic sensing strategies, especially 'blinkered' VOI, to improve sequential decision-making for item selection under uncertainty, outperforming traditional myopic approaches in both theory and practice.

Contribution

It proposes a semi-myopic VOI scheme with an efficient 'blinkered' method, providing better sensing plans and theoretical bounds for selection problems.

Findings

'Blinkered' VOI outperforms pure myopic VOI in experiments.

Theoretical bounds established for special cases.

Empirical results show significant improvement in item selection accuracy.

Abstract

We consider the following sequential decision problem. Given a set of items of unknown utility, we need to select one of as high a utility as possible (``the selection problem''). Measurements (possibly noisy) of item values prior to selection are allowed, at a known cost. The goal is to optimize the overall sequential decision process of measurements and selection. Value of information (VOI) is a well-known scheme for selecting measurements, but the intractability of the problem typically leads to using myopic VOI estimates. In the selection problem, myopic VOI frequently badly underestimates the value of information, leading to inferior sensing plans. We relax the strict myopic assumption into a scheme we term semi-myopic, providing a spectrum of methods that can improve the performance of sensing plans. In particular, we propose the efficiently computable method of ``blinkered'' VOI, and examine theoretical bounds for special cases. Empirical evaluation of ``blinkered'' VOI in the selection problem with normally distributed item values shows that is performs much better than pure myopic VOI.