4.3 Article

Semimyopic Measurement Selection for Optimization Under Uncertainty

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2011.2169247

Keywords

Computational and artificial intelligence; computational intelligence; greedy algorithms; measurement; myopic; non-myopic; optimization; uncertainty; utility; value of information

Funding

  1. IMG4 consortium under Israel Ministry of Trade and Industry
  2. Israel Science Foundation [305/09]
  3. Lynne and William Frankel Center for Computer Sciences

Ask authors/readers for more resources

The following sequential decision problem is considered: given a set of items of unknown utility, an item with as high a utility as possible must be selected (the selection problem). Measurements (possibly noisy) of item features prior to selection are allowed at known costs. 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 VOI, leading to inferior measurement policies. In this paper, the strict myopic assumption is relaxed into a scheme termed semimyopic, providing a spectrum of methods that can improve the performance of measurement policies. In particular, the efficiently computable method of blinkered VOI is proposed, and theoretical bounds for important special cases are examined. Empirical evaluation of blinkered VOI in the selection problem with normally distributed item values shows that it performs much better than pure myopic VOI.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.3
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available