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Almost Sure Productivity

Authors Alejandro Aguirre, Gilles Barthe, Justin Hsu, Alexandra Silva

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Author Details

Alejandro Aguirre
  • IMDEA Software Institute, Madrid, Spain
Gilles Barthe
  • IMDEA Software Institute, Madrid, Spain
Justin Hsu
  • University College London, London, UK
Alexandra Silva
  • University College London, London, UK

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Alejandro Aguirre, Gilles Barthe, Justin Hsu, and Alexandra Silva. Almost Sure Productivity. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 113:1-113:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.ICALP.2018.113

Abstract

We introduce Almost Sure Productivity (ASP), a probabilistic generalization of the productivity condition for coinductively defined structures. Intuitively, a probabilistic coinductive stream or tree is ASP if it produces infinitely many outputs with probability 1. Formally, we define ASP using a final coalgebra semantics of programs inspired by Kerstan and König. Then, we introduce a core language for probabilistic streams and trees, and provide two approaches to verify ASP: a syntactic sufficient criterion, and a decision procedure by reduction to model{-}checking LTL formulas on probabilistic pushdown automata.

Subject Classification

ACM Subject Classification
  • Theory of computation → Denotational semantics
  • Theory of computation → Probabilistic computation
  • Theory of computation → Program reasoning
Keywords
  • Coinduction
  • Probabilistic Programming
  • Productivity

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