Functional Specification of Probabilistic Process Models

Abstract

Agents that handle complex processes evolving over a period of time need to be able to monitor the state of the process. Since the evolution of a process is often stochastic, this re-quires probabilistic monitoring of processes. A probabilistic process modeling language is needed that can adequately cap-ture our uncertainty about the process execution. We present a language for describing probabilistic process models. This language is functional in nature, and the paper argues that a functional language provides a natural way to specify pro-cess models. In our framework, processes have both states and values. Processes may execute sequentially or in par-allel, and we describe two alternative forms of parallelism. An inference algorithm is presented that constructs a dynamic Bayesian network, containing a variable for every subprocess that is executed during the course of executing a process. We present a detailed example demonstrating the naturalness of the language.