Probabilistic Language Framework for Stochastic Discrete Event Systems

Abstract

We introduce the notion of probabilistic languages to describe the qualitative behavior of stochastic discrete event systems. Regular language operators such as choice, concatenation, and Kleene-closure have been defined in the setting of probabilistic language to allow modeling of complex systems in terms of simpler ones. The set of probabilistic languages is closed under such operators thus forming an algebra. It also is a complete partial order under a natural ordering in which the operators are continuous. Hence recursive equations can be solved in this algebra. This fact is alternatively derived by using contraction mapping theorem on the set of probabilistic languages which is shown to be a complete metric space. The notion of regularity of probabilistic languages has also been identified. We show that this formalism is also useful in describing system performances such as completion time, reliability, etc. and present techniques for computing them