Affiliation:
1. Saarland University, Saarbrucken, Germany
Abstract
We propose a framework to prove almost sure termination for probabilistic programs with real valued variables. It is based on ranking supermartingales, a notion analogous to ranking functions on non-probabilistic programs. The framework is proven sound and complete for a meaningful class of programs involving randomization and bounded nondeterminism. We complement this foundational insigh by a practical proof methodology, based on sound conditions that enable compositional reasoning and are amenable to a direct implementation using modern theorem provers. This is integrated in a small dependent type system, to overcome the problem that lexicographic ranking functions fail when combined with randomization. Among others, this compositional methodology enables the verification of probabilistic programs outside the complete class that admits ranking supermartingales.
Funder
Seventh Framework Programme
Deutsche Forschungsgemeinschaft
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Graphics and Computer-Aided Design,Software
Cited by
23 articles.
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1. Stochastic Omega-Regular Verification and Control with Supermartingales;Lecture Notes in Computer Science;2024
2. Omnisemantics: Smooth Handling of Nondeterminism;ACM Transactions on Programming Languages and Systems;2023-03-08
3. Probabilistic Program Verification via Inductive Synthesis of Inductive Invariants;Tools and Algorithms for the Construction and Analysis of Systems;2023
4. Learning Probabilistic Termination Proofs;Computer Aided Verification;2021
5. Proving almost-sure termination by omega-regular decomposition;Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation;2020-06-06