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@COMMENT This file came from Kuldeep S. Meel's publication pages at
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@inproceedings{sm24b,
  title={Model Counting in the Wild},
  abstract={
    Model counting is a fundamental problem in automated reasoning with
    applications in probabilistic inference, network reliability, neural network
    verification, and more. Although model counting is computationally
    intractable from a theoretical perspective due to its $#$P-completeness, the
    past decade has seen significant progress in developing state-of-the-art
    model counters to address scalability challenges.
    In this work, we conduct a rigorous assessment of the scalability of model
    counters in the wild. To this end, we surveyed 11 application domains and
    collected an aggregate of 2262 benchmarks from these domains. We then
    evaluated six state-of-the-art model counters on these instances to assess
    scalability and runtime performance.
    Our empirical evaluation demonstrates that the performance of model counters
    varies significantly across different application domains, underscoring the
    need for careful selection by the end user. Additionally, we investigated
    the behavior of different counters with respect to two parameters suggested
    by the model counting community, finding only a weak correlation. Our
    analysis highlights the challenges and opportunities for portfolio-based
    approaches in model counting.
  },
  author={Shaw, Arijit and Meel, Kuldeep S.},
  year={2024},
  booktitle=KR,
  month=nov,
  bib2html_rescat={Counting},
  bib2html_pubtype={Refereed Conference},
  bib2html_dl_pdf={https://arxiv.org/abs/2408.07059},
}