License: 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

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07161.ZettlemoyerLuke.Paper.1379.pdf (0.2 MB)


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}
}

Keywords: Particle filter, logical hidden Markov model
Collection: 07161 - Probabilistic, Logical and Relational Learning - A Further Synthesis
Issue Date: 2008
Date of publication: 06.03.2008


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