On the Impact of Junction-Tree Topology on Weighted Model Counting

Batya Kenig; Avigdor Gal

doi:10.1007/978-3-319-23540-0_6

On the Impact of Junction-Tree Topology on Weighted Model Counting

Batya Kenig, Avigdor Gal

Technion - Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

We present and evaluate the power of a new framework for weighted model counting and inference in graphical models, based on exploiting the topology of the junction tree representing the formula. The proposed approach uses the junction tree topology in order to craft a reduced set of partial assignments that are guaranteed to decompose the formula. We show that taking advantage of the junction tree structure, along with existing optimization methods borrowed from the CNF-SAT domain, can translate into significant time savings for weighted model counting algorithms.

Original language	English
Title of host publication	SCALABLE UNCERTAINTY MANAGEMENT (SUM 2015)
Editors	Alex Dekhtyar, Christoph Beierle
Pages	83-98
Number of pages	16
Volume	9310
DOIs	https://doi.org/10.1007/978-3-319-23540-0_6
State	Published - 2015
Event	9th International Conference on Scalable Uncertainty Management, SUM 2015 - Quebec City, Canada Duration: 16 Sep 2015 → 18 Sep 2015

Publication series

Name	Lecture Notes in Artificial Intelligence

Conference

Conference	9th International Conference on Scalable Uncertainty Management, SUM 2015
Country/Territory	Canada
City	Quebec City
Period	16/09/15 → 18/09/15

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-23540-0_6

Cite this

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title = "On the Impact of Junction-Tree Topology on Weighted Model Counting",

abstract = "We present and evaluate the power of a new framework for weighted model counting and inference in graphical models, based on exploiting the topology of the junction tree representing the formula. The proposed approach uses the junction tree topology in order to craft a reduced set of partial assignments that are guaranteed to decompose the formula. We show that taking advantage of the junction tree structure, along with existing optimization methods borrowed from the CNF-SAT domain, can translate into significant time savings for weighted model counting algorithms.",

author = "Batya Kenig and Avigdor Gal",

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year = "2015",

doi = "10.1007/978-3-319-23540-0_6",

language = "الإنجليزيّة",

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Kenig, B & Gal, A 2015, On the Impact of Junction-Tree Topology on Weighted Model Counting. in A Dekhtyar & C Beierle (eds), SCALABLE UNCERTAINTY MANAGEMENT (SUM 2015). vol. 9310, Lecture Notes in Artificial Intelligence, pp. 83-98, 9th International Conference on Scalable Uncertainty Management, SUM 2015, Quebec City, Canada, 16/09/15. https://doi.org/10.1007/978-3-319-23540-0_6

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AU - Gal, Avigdor

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

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Y1 - 2015

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M3 - فصل

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BT - SCALABLE UNCERTAINTY MANAGEMENT (SUM 2015)

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T2 - 9th International Conference on Scalable Uncertainty Management, SUM 2015

Y2 - 16 September 2015 through 18 September 2015

ER -

On the Impact of Junction-Tree Topology on Weighted Model Counting

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