  Reasoning about uncertainty is vital in many real-life autonomous systems.
However, current state-of-the-art planning algorithms cannot either reason
about uncertainty explicitly, or do so with a high computational burden. Here,
we focus on making informed decisions efficiently, using reward functions that
explicitly deal with uncertainty. We formulate an approximation, namely an
abstract observation model, that uses an aggregation scheme to alleviate
computational costs. We derive bounds on the expected information-theoretic
reward function and, as a consequence, on the value function. We then propose a
method to refine aggregation to achieve identical action selection with a
fraction of the computational time.
