@inproceedings{d0e3a89cdf504babb8ee5863be20760a,
title = "Constrained sampling and counting: Universal hashing meets SAT solving",
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.",
author = "Meel, {Kuldeep S.} and Vardi, {Moshe Y.} and Supratik Chakraborty and Fremont, {Daniel J.} and Seshia, {Sanjit A.} and Dror Fried and Alexander Ivrii and Sharad Malik",
note = "Publisher Copyright: Copyright {\textcopyright} 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org).; 30th AAAI Conference on Artificial Intelligence, AAAI 2016 ; Conference date: 12-02-2016 Through 13-02-2016",
year = "2016",
language = "English (US)",
series = "AAAI Workshop - Technical Report",
publisher = "AI Access Foundation",
pages = "344--351",
booktitle = "WS-16-01",
address = "United States",
}