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Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.07161.5
URN: urn:nbn:de:0030-drops-13792
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2008/1379/
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Zettlemoyer, Luke S. ;
Pasula, Hanna M. ;
Pack Kaelbling, Leslie
Logical Particle Filtering
Abstract
In this paper, we consider the problem of
filtering in relational hidden Markov models.
We present a compact representation for such models
and an associated logical particle filtering
algorithm. Each particle contains a logical
formula that describes a set of states.
The algorithm updates the formulae as new
observations are received.
Since a single particle tracks many states, this filter
can be more accurate than a traditional particle filter
in high dimensional state spaces, as we demonstrate
in experiments.
BibTeX - Entry
@InProceedings{zettlemoyer_et_al:DagSemProc.07161.5,
author = {Zettlemoyer, Luke S. and Pasula, Hanna M. and Pack Kaelbling, Leslie},
title = {{Logical Particle Filtering}},
booktitle = {Probabilistic, Logical and Relational Learning - A Further Synthesis},
pages = {1--14},
series = {Dagstuhl Seminar Proceedings (DagSemProc)},
ISSN = {1862-4405},
year = {2008},
volume = {7161},
editor = {Luc de Raedt and Thomas Dietterich and Lise Getoor and Kristian Kersting and Stephen H. Muggleton},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2008/1379},
URN = {urn:nbn:de:0030-drops-13792},
doi = {10.4230/DagSemProc.07161.5},
annote = {Keywords: Particle filter, logical hidden Markov model}
}
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Keywords: |
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Particle filter, logical hidden Markov model |
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Collection: |
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07161 - Probabilistic, Logical and Relational Learning - A Further Synthesis |
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Issue Date: |
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2008 |
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Date of publication: |
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06.03.2008 |
