  Active perception strategies enable an agent to selectively gather
information in a way to improve its performance. In applications in which the
agent does not have prior knowledge about the available information sources, it
is crucial to synthesize active perception strategies at runtime. We consider a
setting in which at runtime an agent is capable of gathering information under
a limited budget. We pose the problem in the context of partially observable
Markov decision processes. We propose a generalized greedy strategy that
selects a subset of information sources with near-optimality guarantees on
uncertainty reduction. Our theoretical analysis establishes that the proposed
active perception strategy achieves near-optimal performance in terms of
expected cumulative reward. We demonstrate the resulting strategies in
simulations on a robotic navigation problem.
