A linear process-algebraic format with data for probabilistic automata
This paper presents a novel linear process-algebraic format for probabilistic automata. The key ingredient is a symbolic transformation of probabilistic process algebra terms that incorporate data into this linear format while preserving strong probabilistic bisimulation. This generalises similar techniques for traditional process algebras with data, and - more importantly - treats data and data-dependent probabilistic choice in a fully symbolic manner, leading to the symbolic analysis of parameterised probabilistic systems. We discuss several reduction techniques that can easily be applied to our models. A validation of our approach on two benchmark leader election protocols shows reductions of more than an order of magnitude. (C) 2011 Elsevier B.V. All rights reserved.
symbolic transformations probabilistic process algebra data-dependent probabilistic choice linearisation state space reduction