A Demonic Outcome Logic for Randomized Nondeterminism
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 pape...
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07.01.2025
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 19 |
| Author | Kozen, Dexter Zilberstein, Noam Tassarotti, Joseph Silva, Alexandra |
| Author_xml | – sequence: 1 givenname: Noam orcidid: 0000-0001-6388-063X surname: Zilberstein fullname: Zilberstein, Noam email: noamz@cs.cornell.edu organization: Cornell University, Ithaca, USA – sequence: 2 givenname: Dexter orcidid: 0000-0002-8007-4725 surname: Kozen fullname: Kozen, Dexter email: kozen@cs.cornell.edu organization: Cornell University, Ithaca, USA – sequence: 3 givenname: Alexandra orcidid: 0000-0001-5014-9784 surname: Silva fullname: Silva, Alexandra email: alexandra.silva@cornell.edu organization: Cornell University, Ithaca, USA – sequence: 4 givenname: Joseph orcidid: 0000-0001-5692-3347 surname: Tassarotti fullname: Tassarotti, Joseph email: jt4767@nyu.edu organization: New York University, New York, USA |
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Darmstadt University of Technology, Germany. isbn:978-3-8265-6396-6 https://d-nb.info/957239157 Filippo Bonchi, Ana Sokolova, and Valeria Vignudelli. e_1_2_2_4_1 e_1_2_2_24_1 e_1_2_2_49_1 e_1_2_2_6_1 e_1_2_2_22_1 den Hartog Jerry (e_1_2_2_14_1) e_1_2_2_20_1 e_1_2_2_2_1 Corin Ricardo (e_1_2_2_11_1) e_1_2_2_41_1 e_1_2_2_43_1 e_1_2_2_64_1 e_1_2_2_8_1 e_1_2_2_28_1 e_1_2_2_45_1 e_1_2_2_66_1 e_1_2_2_26_1 e_1_2_2_47_1 e_1_2_2_38_1 e_1_2_2_59_1 e_1_2_2_30_1 e_1_2_2_51_1 e_1_2_2_19_1 e_1_2_2_32_1 e_1_2_2_53_1 e_1_2_2_17_1 e_1_2_2_34_1 e_1_2_2_55_1 e_1_2_2_15_1 e_1_2_2_36_1 e_1_2_2_57_1 e_1_2_2_25_1 e_1_2_2_48_1 e_1_2_2_5_1 e_1_2_2_23_1 e_1_2_2_7_1 e_1_2_2_21_1 e_1_2_2_1_1 e_1_2_2_3_1 e_1_2_2_40_1 e_1_2_2_63_1 e_1_2_2_42_1 e_1_2_2_65_1 e_1_2_2_9_1 e_1_2_2_29_1 e_1_2_2_44_1 e_1_2_2_67_1 Dijkstra Edsger W. (e_1_2_2_18_1) e_1_2_2_27_1 e_1_2_2_46_1 von Neumann John (e_1_2_2_60_1) 1951 Zilberstein Noam (e_1_2_2_62_1) 2024 e_1_2_2_61_1 e_1_2_2_37_1 e_1_2_2_12_1 e_1_2_2_39_1 e_1_2_2_10_1 den Hartog Jerry (e_1_2_2_13_1) e_1_2_2_52_1 e_1_2_2_31_1 e_1_2_2_54_1 e_1_2_2_33_1 e_1_2_2_56_1 e_1_2_2_16_1 e_1_2_2_35_1 e_1_2_2_58_1 e_1_2_2_50_1 |
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