Reference:

Rémi Bonnet, Stefan Kiefer, and Anthony W. Lin. Analysis of probabilistic basic parallel processes. In Anca Muscholl, editor, Proceedings of the 17th International Conference on Foundations of Software Science and Computation Structures (FoSSaCS), volume 8412 of LNCS, pages 43–57, Grenoble, France, 2014. Springer.

Abstract:

Basic Parallel Processes (BPPs) are a well-known subclass of Petri Nets. They are the simplest common model of concurrent programs that allows unbounded spawning of processes. In the probabilistic version of BPPs, every process generates other processes according to a probability distribution. We study the decidability and complexity of fundamental qualitative problems over probabilistic BPPs – in particular reachability with probability 1 of different classes of target sets (e.g. upward-closed sets). Our results concern both the Markov-chain model, where processes are scheduled randomly, and the MDP model, where processes are picked by a scheduler.

Suggested BibTeX entry:

@inproceedings{14BKL-FOSSACS,
    address = {Grenoble, France},
    author = {R{\'e}mi Bonnet and Stefan Kiefer and Anthony W. Lin},
    booktitle = {Proceedings of the 17th International Conference on Foundations of Software Science and Computation Structures (FoSSaCS)},
    editor = {Anca Muscholl},
    pages = {43--57},
    publisher = {Springer},
    series = {LNCS},
    title = {Analysis of Probabilistic Basic Parallel Processes},
    volume = {8412},
    year = {2014}
}