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@COMMENT This file came from Kuldeep S. Meel's publication pages at
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@inproceedings{MCVF15,
  title={Constrained Sampling and Counting: Universal Hashing meets SAT Solving},
  bib2html_dl_pdf={../Papers/BNP16.pdf},
  code={https://bitbucket.org/kuldeepmeel/unigen},
  author={Meel, Kuldeep S. and Vardi, Moshe Y. and  Chakraborty, Supratik and Fremont, Daniel J. and Seshia, Sanjit A. and Fried, Dror and Ivrii, Alexander and Malik, Sharad},
  booktitle=BNP,
  year={2016},
    bib2html_rescat={Sampling,Counting},
  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.},
  month=feb,
  bib2html_pubtype={workshop},
}