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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