ece kamar and eric horvitz
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 . 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.
|Published in||Twelth international conference on autonomous agents and mutli-agent systems|