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@COMMENT This file came from Kuldeep S. Meel's publication pages at
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@inproceedings{CGSM20,
  author={
    Ciampiconi, Lorenzo and Ghosh, Bishwamittra and Scarlett, Jonathan and Meel,
    Kuldeep S.
  },
  title={A {MaxSAT}-based Framework for Group Testing},
  booktitle=AAAI,
  month=feb,
  year={2020},
  bib2html_pubtype={Refereed Conference},
  bib2html_dl_pdf={../Papers/aaai20-cgsm.pdf},
  code={https://github.com/meelgroup/mgt},
  bib2html_rescat={Formal Methods 4 ML},
  abstract={
    The success of MaxSAT (maximum satisfiability) solving in recent years has
    motivated researchers to apply
    MaxSAT solvers in diverse discrete combinatorial optimization problems.
    Group testing has been studied as a combinatorial
    optimization problem, where the goal is to find defective items among a set
    of items by performing sets of tests on items.
    In this paper, we propose a MaxSAT-based framework, called MGT, that solves
    group testing, in particular, the decoding
    phase of non-adaptive group testing. We extend this approach to the noisy
    variant of group testing, and propose a compact
    MaxSAT-based encoding that guarantees an optimal solution. Our extensive
    experimental results show that MGT can solve group
    testing instances of 10000 items with 3% defectivity, which no prior work
    can handle to the best of our knowledge. Furthermore,
    MGT has better accuracy than the LP-based ap- proach. We also discover an
    interesting phase transition behavior in the runtime,
    which reveals the easy-hard-easy nature of group testing.
  },
}