@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
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@inproceedings{km24,
  title={On Lower Bounding Minimal Model Count},
  abstract={
    Minimal models of a Boolean formula play a pivotal role in various reasoning
    tasks. While
    previous research has primarily focused on qualitative analysis over minimal
    models; our study
    concentrates on the quantitative aspect, specifically counting of minimal
    models. Exact counting
    of minimal models is strictly harder than #P, prompting our investigation
    into establishing a
    lower bound for their quantity, which is often useful in related
    applications. In this paper, we
    introduce two novel techniques for counting minimal models, leveraging the
    expressive power of
    answer set programming: the first technique employs methods from knowledge
    compilation, while
    the second one draws on recent advancements in hashing-based approximate
    model counting.
    Through empirical evaluations, we demonstrate that our methods significantly
    improve the
    lower bound estimates of the number of minimal models, surpassing the
    performance of existing
    minimal model reasoning systems in terms of runtime.
  },
  author={Kabir, Mohimenul and Meel, Kuldeep S.},
  year={2024},
  booktitle=ICLP,
  month=nov,
  bib2html_rescat={Counting},
  bib2html_pubtype={Refereed Conference,Award Winner},
  note={Best Paper Award},
  bib2html_dl_pdf={https://arxiv.org/pdf/2407.09744},
}