Light at the End of the Tunnel: A Monte Carlo Approach to Computing Value of Information

ece kamar and eric horvitz

Abstract

Calculating the expected value of information (VOI) for se-

quences of observations under uncertainty is intractable, as

branching trees of potential outcomes of sets of observations

must be considered in the general case [11]. We address

the combinatorial challenge of computing ideal observational

policies in situations where long sequences of weak evidential

updates may have to be considered. We introduce and vali-

date the use of Monte Carlo procedures for computing VOI

with such long evidential sequences. We evaluate the pro-

cedure on a synthetic dataset and on a challenging citizen-

science problem and demonstrate how it can effectively cut

through the intractability of the combinatorial space.

Details

Publication typeProceedings
Published inTwelth international conference on autonomous agents and mutli-agent systems
PublisherACM
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