@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.},
}