Lifted Probabilistic Inference with Counting Formulas
Name
Milch-2008-Lifted Probablistic Inference.pdf
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Author(s)
Haimes, Michael M.
Kaelbling, Leslie P.
Kersting, Kristian
Milch, Brian
Zettlemoyer, Luke S.
Date Issued
January 2008
Journal
Proceedings of the 23rd National Conference on Artificial Intelligence, (AAAI '08)
Publisher
AAAI Press
Citation
Haimes, Michael M., et al. "Lifted probabilistic inference with counting formulas." Proceedings of the 23rd National Conference on Artificial Intelligence (2008): 1062-1068. © 2008 AAAI Press
Version
Final published version
Abstract
Lifted inference algorithms exploit repeated structure in probabilistic models to answer queries efficiently. Previous work such as de Salvo Braz et al.'s first-order variable elimination (FOVE) has focused on the sharing of potentials across interchangeable random variables. In this paper, we also exploit interchangeability within individual potentials by introducing counting formulas, which indicate how many of the random variables in a set have each possible value. We present a new lifted inference algorithm, C-FOVE, that not only handles counting formulas in its input, but also creates counting formulas for use in intermediate potentials. C-FOVE can be described succinctly in terms of six operators, along with heuristics for when to apply them. Because counting formulas capture dependencies among large numbers of variables compactly, C-FOVE achieves asymptotic speed improvements compared to FOVE.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://dl.acm.org/citation.cfm?id=1620237
