Introduction to probabilistic automata

Abstract

This paper provides an elementary introduction to the probabilistic automaton (PA) model, which has been developed by Segala. We describe how distributed systems with discrete probabilities can be modeled and analyzed by means of PAs. We explain how the basic concepts for the analysis of nonprobabilistic automata can be extended to probabilistic systems. In particular, we treat the parallel composition operator on PAs, the semantics of a PA as a set of trace distributions, an extension of the PA model with time and simulation relations for PAs. Finally, we give an overview of various other state based models that are used for the analysis of probabilistic systems.