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@inproceedings{KESHFM22,
  title={ApproxASP - A Scalable Approximate Answer Set Counter},
  author={
    Kabir, Mohimenul and Everardo, Flavio and
    Shukla, Ankit K. and Hecher, Markus and Fichte, Johannes and Meel, Kuldeep
    S.
  },
  booktitle=AAAI,
  month=feb,
  year={2022},
  bib2html_rescat={Counting},
  bib2html_dl_pdf={../Papers/aaai22-keskhm.pdf},
  bib2html_pubtype={Refereed Conference},
  abstract={
    Answer Set Programming (ASP) is a framework in artificial intelligence and
    knowledge representation for declarative modeling and problem solving.
    Modern ASP solvers focus on the computation or enumeration of answer sets.
    However, a variety of probabilistic applications in reasoning or logic
    programming require counting answer sets. While counting can be done by
    enumeration, simple enumeration becomes immediately infeasible if the number
    of solutions is high. On the other hand, approaches to exact counting are of
    high worst-case complexity. In fact, in propositional model counting, exact
    counting becomes impractical. In this work, we present a scalable approach
    to approximate counting for answer set programming. Our approach is based on
    systematically adding XOR constraints to ASP programs, which divide the
    search space. We prove that adding random XOR constraints partitions the
    answer sets of an ASP program. In practice, we use a Gaussian
    elimination-based approach by lifting ideas from SAT to ASP and integrating
    it into a state of the art ASP solver, which we call ApproxASP. Finally, our
    experimental evaluation shows the scalability of our approach over the
    existing ASP systems.
  },
}