@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@COMMENT This file came from Kuldeep S. Meel's publication pages at
@COMMENT http://www.comp.nus.edu.sg/~meel/publications/
@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},
}