POMDPs under Probabilistic Semantics

We consider partially observable Markov decision processes (POMDPs) with limitaverage payoff, where a reward value in the interval [0, 1] is associated to every transition, and the payoff of an infinite path is the long-run average of the rewards. We consider two types of path constraints: (i) quantitative constraint defines the set of paths where the payoff is at least a given threshold λ1 ∈ (0, 1]; and (ii) qualitative constraint which is a special case of quantitative constraint with λ1 = 1. We consider the computation of the almost-sure winning set, where the controller needs to ensure that the path constraint is satisfied with probability 1. Our main results for qualitative path constraint are as follows: (i) the problem of deciding the existence of a finite-memory controller is EXPTIME-complete; and (ii) the problem of deciding the existence of an infinite-memory controller is undecidable. For quantitative path constraint we show that the problem of deciding the existence of a finite-memory controller is undecidable.

Authors

• Krishnendu Chatterjee
• Martin Chmelik

Keywords

No keywords are indexed for this paper.