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Probabilistic Analysis of Binary Sessions

Authors Omar Inverso , Hernán Melgratti , Luca Padovani , Catia Trubiani , Emilio Tuosto

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ACM Subject Classification
  • Theory of computation → Type structures
Keywords
  • Probabilistic choices
  • session types
  • static analysis
  • deadlock freedom

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Abstract

We study a probabilistic variant of binary session types that relate to a class of Finite-State Markov Chains. The probability annotations in session types enable the reasoning on the probability that a session terminates successfully, for some user-definable notion of successful termination. We develop a type system for a simple session calculus featuring probabilistic choices and show that the success probability of well-typed processes agrees with that of the sessions they use. To this aim, the type system needs to track the propagation of probabilistic choices across different sessions.

Cite As Get BibTex

Omar Inverso, Hernán Melgratti, Luca Padovani, Catia Trubiani, and Emilio Tuosto. Probabilistic Analysis of Binary Sessions. In 31st International Conference on Concurrency Theory (CONCUR 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 171, pp. 14:1-14:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.CONCUR.2020.14

Author Details

Omar Inverso
  • Gran Sasso Science Institute, L'Aquila, Italy
Hernán Melgratti
  • ICC, Universidad de Buenos Aires, Conicet, Argentina
Luca Padovani
  • Università di Torino, Italy
Catia Trubiani
  • Gran Sasso Science Institute, L'Aquila, Italy
Emilio Tuosto
  • Gran Sasso Science Institute, L'Aquila, Italy

Funding

Hernán Melgratti, Luca Padovani and Emilio Tuosto have been partially supported by EU H2020 RISE programme under the Marie Skłodowska-Curie grant agreement No 778233.
  • Inverso, Omar: Omar Inverso has been partially supported by MIUR project PRIN 2017FTXR7S IT MATTERS (Methods and Tools for Trustworthy Smart Systems).
  • Melgratti, Hernán: Hernán Melgratti has been partially supported by UBACyT projects 20020170100544BA and 20020170100086BA and PIP project 11220130100148CO.
  • Trubiani, Catia: Catia Trubiani has been partially supported by MIUR project PRIN 2017TWRCNB SEDUCE (Designing Spatially Distributed Cyber-Physical Systems under Uncertainty).

Acknowledgements

#2{The authors are grateful to the anonymous reviewers for their detailed feedback. }

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