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. © 2014 Springer-Verlag.
CITATION STYLE
Bonnet, R., Kiefer, S., & Lin, A. W. (2014). Analysis of probabilistic basic parallel processes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8412 LNCS, pp. 43–57). Springer Verlag. https://doi.org/10.1007/978-3-642-54830-7_3
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