Point-Based Methods for Model Checking in Partially Observable Markov
  Decision Processes
  Autonomous systems are often required to operate in partially observable
environments. They must reliably execute a specified objective even with
incomplete information about the state of the environment. We propose a
methodology to synthesize policies that satisfy a linear temporal logic formula
in a partially observable Markov decision process (POMDP). By formulating a
planning problem, we show how to use point-based value iteration methods to
efficiently approximate the maximum probability of satisfying a desired logical
formula and compute the associated belief state policy. We demonstrate that our
method scales to large POMDP domains and provides strong bounds on the
performance of the resulting policy.
