Equational axioms for probabilistic bisimilarity

Luca Aceto, Z. Ésik, Anna Ingólfsdóttir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

This paper gives an equational axiomatization of probabilistic bisimulation equivalence for a class of finite-state agents previously studied by Stark and Smolka ((2000) Proof, Language, and Interaction: Essays in Honour of Robin Milner, pp. 571–595). The axiomatization is obtained by extending the general axioms of iteration theories (or iteration algebras), which characterize, among others, the equational properties of the fixed point operator on (ω-)continuous or monotonic functions, with three axiom schemas that express laws that are specific to probabilistic bisimilarity. Hence probabilistic bisimilarity (over finite-state agents) has an equational axiomatization relative to iteration algebras.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages239-254
Number of pages16
Volume2422
ISBN (Print)9783540441441
Publication statusPublished - 2002
Event9th International Conference on Algebraic Methodology and SoftwareTechnology, AMAST 2002 - Reunion Island, Saint-Gilles-les-Bains, France
Duration: Sep 9 2002Sep 13 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2422
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other9th International Conference on Algebraic Methodology and SoftwareTechnology, AMAST 2002
CountryFrance
CityReunion Island, Saint-Gilles-les-Bains
Period9/9/029/13/02

Fingerprint

Axiomatization
Axioms
Algebra
Iteration
Mathematical operators
Monotonic Function
Bisimulation
Axiom
Schema
Continuous Function
Express
Fixed point
Equivalence
Operator
Interaction

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Aceto, L., Ésik, Z., & Ingólfsdóttir, A. (2002). Equational axioms for probabilistic bisimilarity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2422, pp. 239-254). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2422). Springer Verlag.

Equational axioms for probabilistic bisimilarity. / Aceto, Luca; Ésik, Z.; Ingólfsdóttir, Anna.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2422 Springer Verlag, 2002. p. 239-254 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2422).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Aceto, L, Ésik, Z & Ingólfsdóttir, A 2002, Equational axioms for probabilistic bisimilarity. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 2422, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2422, Springer Verlag, pp. 239-254, 9th International Conference on Algebraic Methodology and SoftwareTechnology, AMAST 2002, Reunion Island, Saint-Gilles-les-Bains, France, 9/9/02.
Aceto L, Ésik Z, Ingólfsdóttir A. Equational axioms for probabilistic bisimilarity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2422. Springer Verlag. 2002. p. 239-254. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Aceto, Luca ; Ésik, Z. ; Ingólfsdóttir, Anna. / Equational axioms for probabilistic bisimilarity. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2422 Springer Verlag, 2002. pp. 239-254 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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