Partially observable Markov decision processes (POMDPs) provide an elegant mathematical
framework for modeling complex decision and planning problems in stochastic
domains in which states of the system are observable only indirectly, via a set of imperfect
or noisy observations. The modeling advantage of POMDPs, however, comes at a price |
exact methods for solving them are computationally very expensive and thus applicable
in practice only to very simple problems. We focus on ecient...