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.
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Keywords
- Markov Chain
- Markov Decision Process
- Coverability Problem
- Reachability Problem
- Markov Decision Process Model
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Bonnet, R., Kiefer, S., Lin, A.W. (2014). Analysis of Probabilistic Basic Parallel Processes. In: Muscholl, A. (eds) Foundations of Software Science and Computation Structures. FoSSaCS 2014. Lecture Notes in Computer Science, vol 8412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54830-7_3
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