Title: Learning to Acquire Information,
Abstract: We consider the problem of diagnosis where a set of simple observations are
used to infer a potentially complex hidden hypothesis. Finding the optimal
subset of observations is intractable in general, thus we focus on the problem
of active diagnosis, where the agent selects the next most-informative
observation based on the results of previous observations. We show that under
the assumption of uniform observation entropy, one can build an implication
model which directly predicts the outcome of the potential next observation
conditioned on the results of past observations, and selects the observation
with the maximum entropy. This approach enjoys reduced computation complexity
by bypassing the complicated hypothesis space, and can be trained on
observation data alone, learning how to query without knowledge of the hidden
hypothesis.