# Stochastic Decision Petri Nets

arXiv: https://arxiv.org/abs/2303.13344 · HTML: https://mlsanity.com/paper/2303.13344/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2023.

## Built on by (most cited first, 1 of 1)

- [Process mining-driven modeling and simulation to enhance fault diagnosis in cyber-physical systems](https://mlsanity.com/paper/2506.21502/) (2025)

## Abstract (arXiv)

We introduce stochastic decision Petri nets (SDPNs), which are a form of stochastic Petri nets equipped with rewards and a control mechanism via the deactivation of controllable transitions. Such nets can be translated into Markov decision processes (MDPs), potentially leading to a combinatorial explosion in the number of states due to concurrency. Hence we restrict ourselves to instances where nets are either safe, free-choice and acyclic nets (SAFC nets) or even occurrence nets and policies are defined by a constant deactivation pattern. We obtain complexity-theoretic results for such cases via a close connection to Bayesian networks, in particular we show that for SAFC nets the question whether there is a policy guaranteeing a reward above a certain threshold is mathsfNP^mathsfPP-complete. We also introduce a partial-order procedure which uses an SMT solver to address this problem.