@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@COMMENT This file came from Kuldeep S. Meel's publication pages at
@COMMENT http://www.comp.nus.edu.sg/~meel/publications/
@article{PDMV19b,
  author={
    Paredes, Roger and Duenas-Osorio, Leonardo and Meel, Kuldeep S. and Vardi,
    Moshe Y.
  },
  title={
    Principled network reliability approximation: A counting-based approach.
  },
  journal=RESS,
  bib2html_pubtype={Journal},
  month=nov,
  year={2019},
  bib2html_dl_pdf={../Papers/ress.pdf},
  bib2html_rescat={Counting},
  abstract={
    As engineered systems expand, become more interdependent, and operate in
    real-time, reliability
    assessment is indispensable to support investment and decision making.
    However, network reliability problems
    are known to be #P-complete, a computational complexity class largely
    believed to be intractable. The computational
    intractability of network reliability motivates our quest for reliable
    approximations. Based on their theoretical
    foundations, available methods can be grouped as follows: (i) exact or
    bounds, (ii) guarantee-less sampling, and (iii)
    probably approximately correct (PAC). Group (i) is well regarded due to its
    useful byproducts, but it does not scale in
    practice. Group (ii) scales well and verifies desirable properties, such as
    the bounded relative error, but it lacks
    error guarantees. Group (iii) is of great interest when precision and
    scalability are required, as it harbors computationally
    feasible approximation schemes with PAC-guarantees. We give a comprehensive
    review of classical methods before introducing
    modern techniques and our developments. We introduce K-RelNet, an extended
    counting-based estimation method that delivers
    PAC-guarantees for the K-terminal reliability problem. Then, we test
    methods' performance using various benchmark systems.
    We highlight the range of application of algorithms and provide the
    foundation for
    future resilience engineering as it increasingly necessitates methods for
    uncertainty quantification in complex systems.
  },
}