  This paper is concerned with planning in stochastic domains by means of
partially observable Markov decision processes (POMDPs). POMDPs are difficult
to solve. This paper identifies a subclass of POMDPs called region observable
POMDPs, which are easier to solve and can be used to approximate general POMDPs
to arbitrary accuracy.
