Polynomial Analysis Algorithms for Free Choice Probabilistic Workflow Nets.

We introduce Probabilistic Workflow Nets (PWNs), a model extending confusion-free workflow Petri nets with probabilities. We give PWNs a semantics in terms of Markov Decision Processes (MDPs) and introduce a reward model. We show that the expected reward of a complete execution of a PWN is independent of the scheduler used to resolve the nondeterminism of the MDP, which allows one to choose a suitable scheduler for its computation. However, this feature does not lead to a polynomial algorithm, and in fact we prove that deciding whether the expected reward exceeds a given threshold is PSPACE-hard.