Region-Based Approximations for Planning in Stochastic Domains
  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.
