A Demonic Outcome Logic for Randomized Nondeterminism

作     者：Zilberstein, Noam Kozen, Dexter Silva, Alexandra Tassarotti, Joseph 

作者机构：Cornell Univ Ithaca NY 14850 USA NYU New York NY USA 

出 版 物：《PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL》 (Proc. ACM Program. Lang.)

年 卷 期：2025年第9卷第POPL期

页      面：539-568页

核心收录：

基　　金：ERC ARIA's Safeguarded AI programme NSF [CCF-2008083] 101002697 

主　　题：Program Logics Probabilistic Programming Demonic Nondeterminism 

摘      要：Programs increasingly rely on randomization in applications such as cryptography and machine learning. Analyzing randomized programs has been a fruitful research direction, but there is a gap when programs also exploit nondeterminism (for concurrency, efficiency, or algorithmic design). In this paper, we introduce Demonic Outcome Logic for reasoning about programs that exploit both randomization and nondeterminism. The logic includes several novel features, such as reasoning about multiple executions in tandem and manipulating pre- and postconditions using familiar equational laws-including the distributive law of probabilistic choices over nondeterministic ones. We also give rules for loops that both establish termination and quantify the distribution of final outcomes from a single premise. We illustrate the reasoning capabilities of Demonic Outcome Logic through several case studies, including the Monty Hall problem, an adversarial protocol for simulating fair coins, and a heuristic based probabilistic SAT solver.