From weighted to unweighted model counting

Supratik Chakraborty, Dror Fried, Kuldeep S. Meel, Moshe Y. Vardi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationIJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
EditorsMichael Wooldridge, Qiang Yang
Pages689-695
Number of pages7
ISBN (Electronic)9781577357384
StatePublished - 2015
Externally publishedYes
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: 25 Jul 201531 Jul 2015

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2015-January

Conference

Conference24th International Joint Conference on Artificial Intelligence, IJCAI 2015
Country/TerritoryArgentina
CityBuenos Aires
Period25/07/1531/07/15

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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