Reactive, Generative and Stratified Models of Probabilistic Processes,

Abstract

We introduce three models of probabilistic processes, namely, reactive, generative and stratified. These models are investigated within the context of PCCS, an extension of Milner's SCCS in which each summand of a process summation expression is guarded by a probability and the sum of these probabilities is 1. For each model we present a structural operational semantics of PCCS and a notion of bisimulation equivalence which we prove to be a congruence. We also show that the models form a hierarchy: the reactive model is derivable from the generative model by abstraction from the relative probabilities of different actions, and the generative model is derivable from the stratified model by abstraction from the purely probabilistic branching structure. Moreover the classical nonprobabilistic model is derivable from each of these models by abstraction from all probabilities.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1994
Accession Number
ADA325957

Entities

People

  • Bernhard Steffen
  • Rob J. Van Glabbeek
  • Scott A. Smolka

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Classification
  • Computer Programming
  • Computer Science
  • Computers
  • Generative Models
  • Hierarchies
  • Language
  • Models
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Semantics
  • Stochastic Processes
  • Theoretical Computer Science

Fields of Study

  • Mathematics

Readers

  • Computational Linguistics
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms