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From Weighted to Unweighted Model Counting.
Supratik Chakraborty, Dror Fried, Kuldeep S. Meel and Moshe Y. Vardi.
In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), July 2015.
The recent surge of interest in reasoning aboutprobabilistic graphical models has led to the developmentof various techniques for probabilisticreasoning. Of these, techniques based on weightedmodel counting are particularly interesting sincethey can potentially leverage recent advances in unweightedmodel counting and in propositional satisfiabilitysolving. In this paper, we present a newapproach to weighted model counting via reductionto unweighted model counting. Our reduction,which is polynomial-time and preserves the normalform (CNF/DNF) of the input formula, allows usto exploit advances in unweighted model countingto solve weighted model counting instances. Experimentswith weighted model counters built usingour reduction indicate that these counters performsmuch better than a state-of-the-art weighted modelcounter.
@inproceedings{CFMV15,
title = {From Weighted to Unweighted Model Counting},
url = {../Papers/ijcai15.pdf},
code={https://bitbucket.org/kuldeepmeel/weightcount},
author={Chakraborty, Supratik and Fried, Dror and Meel, Kuldeep S. and Vardi, Moshe Y.},
booktitle=IJCAI,
year={2015},
month=jul,
bib2html_rescat={Counting},
bib2html_pubtype={Refereed Conference},
abstract={The recent surge of interest in reasoning about
probabilistic graphical models has led to the development
of various techniques for probabilistic
reasoning. Of these, techniques based on weighted
model counting are particularly interesting since
they can potentially leverage recent advances in unweighted
model counting and in propositional satisfiability
solving. In this paper, we present a new
approach to weighted model counting via reduction
to unweighted model counting. Our reduction,
which is polynomial-time and preserves the normal
form (CNF/DNF) of the input formula, allows us
to exploit advances in unweighted model counting
to solve weighted model counting instances. Experiments
with weighted model counters built using
our reduction indicate that these counters performs
much better than a state-of-the-art weighted model
counter.},
}
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