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Axiomatizing probabilistic processes: ACP with generative probabilities

Extended abstract
  • J. C. M. Baeten
  • J. A. Bergstra
  • S. A. Smolka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 630)

Abstract

This paper is concerned with finding complete axiomatizations of probabilistic processes. We examine this problem within the context of the process algebra ACP and obtain as our end-result the axiom system prACP I −- , a probabilistic version of ACP which can be used to reason algebraically about the reliability and performance of concurrent systems. Our goal was to introduce probability into ACP in as simple a fashion as possible. Optimally, ACP should be the homomorphic image of the probabilistic version in which the probabilities are forgotten.

We begin by weakening slightly ACP to obtain the axiom system ACP I The main difference between ACP and ACP I is that the axiom x+δ=x, which does not yield a plausible interpretation in the generative model of probabilistic computation, is rejected in ACP I . We argue that this does not affect the usefulness of ACP I in practice, and show how ACP can be reconstructed from ACP I with a minimal amount of technical machinery.

prACP I is obtained from ACP I through the introduction of probabilistic alternative and parallel composition operators, and a process graph model for prACP I based on probabilistic bisimulation is developed. We show that prACP I is a sound and complete axiomatization of probabilistic bisimulation for finite processes, and that prACP I can be homomorphically embedded in ACP I as desired.

Our results for ACP I and prACP I are presented in a modular fashion by first considering several subsets of the signatures. We conclude with a discussion about the suitability of an internal probabilistic choice operator in the context of prACP I .

Keywords

Axiom System Probabilistic Process Stratify Model Probabilistic Version Complete Axiomatization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • J. C. M. Baeten
    • 1
  • J. A. Bergstra
    • 2
  • S. A. Smolka
    • 3
  1. 1.Dept. of Math and Computing ScienceEindhoven University of TechnologyMB EindhovenThe Netherlands
  2. 2.Programming Research GroupUniversity of AmsterdamDB AmsterdamThe Netherlands
  3. 3.Department of Computer ScienceSUNY at Stony BrookStony BrookUSA

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