Semi-Myopic Sensing Plans for Value Optimization
  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.
