Partially observable Markov decision processes (POMDPs) are a natural model for scenarios where one has to deal with incomplete knowledge and random events.
 Applications include, but are not limited to, robotics and motion planning.
 However, many relevant properties of POMDPs are either undecidable or very expensive to compute in terms of both runtime and memory consumption.
 In our work, we develop a game-based abstraction method that is able to deliver safe bounds and tight
  approximations for important sub-classes of such properties.
 We discuss the theoretical implications and showcase the applicability of our results on a broad spectrum of benchmarks.

This paper provides a game-based abstraction scheme to compute provably sound policies for POMDPs.