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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 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.
@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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