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@COMMENT This file came from Kuldeep S. Meel's publication pages at
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@inproceedings{LMY25,
  title={Panini: An Efficient and Flexible Knowledge Compiler},
  author={Lai, Yong and Meel, Kuldeep S. and Yap, Roland H. C.},
  booktitle=CAV,
  pages={92--105},
  year={2025},
  month=jul,
  bib2html_rescat={Counting, Knowledge Compilation},
  bib2html_pubtype={Refereed Conference},
  bib2html_dl_pdf={https://arxiv.org/pdf/2202.10025},
  abstract={
    Knowledge compilation concerns with the compilation of representation
    languages to target languages supporting a wide range of tractable
    operations arising from diverse areas of computer science. Tractable target
    compilation languages are usually achieved by restrictions on the internal
    nodes of the NNF. In this paper, we propose a new representation language
    CCDD, which introduces new restrictions on conjunction nodes to capture
    equivalent literals. We show that CCDD supports two key queries, model
    counting and uniform samping, in polytime. We present algorithms and a
    compiler to compile propositional formulas expressed in CNF into CCDD.
    Experiments over a large set of benchmarks show that our compilation times
    are better with smaller representation than state-of-art Decision-DNNF, SDD
    and OBDD[AND] compilers. We apply our techniques to model counting and
    uniform sampling, and develop model counter and uniform sampler on CNF. Our
    empirical evaluation demonstrates the following significant improvements:
    our model counter can solve 885 instances while the prior state of the art
    solved only 843 instances, representing an improvement of 43 instances; and
    our uniform sampler can solve 780 instances while the prior state of the art
    solved only 648 instances, representing an improvement of 132 instances.
  },
}