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Constrained sampling and counting: Universal hashing meets SAT solving

Author(s): Meel, KS; Vardi, MY; Chakraborty, S; Fremont, DJ; Seshia, SA; et al

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Abstract: Constrained sampling and counting are two fundamental problems in artificial intelligence with a diverse range of applications, spanning probabilistic reasoning and planning to constrained-random verification. While the theory of these problems was thoroughly investigated in the 1980s, prior work either did not scale to industrial size instances or gave up correctness guarantees to achieve scalability. Recently, we proposed a novel approach that combines universal hashing and SAT solving and scales to formulas with hundreds of thousands of variables without giving up correctness guarantees. This paper provides an overview of the key ingredients of the approach and discusses challenges that need to be overcome to handle larger real-world instances.
Publication Date: 29-Mar-2016
Citation: Meel, KS, Vardi, MY, Chakraborty, S, Fremont, DJ, Seshia, SA, Fried, D, Ivrii, A, Malik, S. (2016). Constrained sampling and counting: Universal hashing meets SAT solving. WS-16-01 - WS-16-15 (344 - 351
Pages: 344 - 351
Type of Material: Conference Article
Series/Report no.: 30th AAAI Conference on Artificial Intelligence, AAAI 2016; Phoenix; United States; 12 February 2016 through 13 February 2016;
Journal/Proceeding Title: AAAI Workshop - Technical Report
Version: Author's manuscript

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