Analysis of Probabilistic Basic Parallel Processes release_b4myall445aiffupcgf7nxlxdq

by Rémi Bonnet and Stefan Kiefer and Anthony W. Lin

Released as a article .

2014  

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.
In text/plain format

Archived Files and Locations

application/pdf  312.0 kB
file_ubiepeuaongbpfugpwwpq5wjpy
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2014-01-16
Version   v1
Language   en ?
arXiv  1401.4130v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 4309cf7e-b57e-4e28-9df7-226e3fe11ce1
API URL: JSON