Programmatic Strategy Synthesis: Resolving Nondeterminism in Probabilistic Programs
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Published:2024-01-05
Issue:POPL
Volume:8
Page:2792-2820
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ISSN:2475-1421
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Container-title:Proceedings of the ACM on Programming Languages
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language:en
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Short-container-title:Proc. ACM Program. Lang.
Author:
Batz Kevin1ORCID,
Biskup Tom Jannik1ORCID,
Katoen Joost-Pieter1ORCID,
Winkler Tobias1ORCID
Affiliation:
1. RWTH Aachen University, Aachen, Germany
Abstract
We consider imperative programs that involve both randomization and pure nondeterminism. The central question is how to find a strategy resolving the pure nondeterminism such that the so-obtained determinized program satisfies a given quantitative specification, i.e., bounds on expected outcomes such as the expected final value of a program variable or the probability to terminate in a given set of states. We show how memoryless and deterministic (MD) strategies can be obtained in a semi-automatic fashion using deductive verification techniques. For loop-free programs, the MD strategies resulting from our weakest precondition-style framework are correct by construction. This extends to loopy programs, provided the loops are equipped with suitable loop invariants - just like in program verification. We show how our technique relates to the well-studied problem of obtaining strategies in countably infinite Markov decision processes with reachability-reward objectives. Finally, we apply our technique to several case studies.
Funder
European Research Council
Deutsche Forschungsgemeinschaft
Publisher
Association for Computing Machinery (ACM)
Subject
Safety, Risk, Reliability and Quality,Software
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