DagSemProc.07161.5.pdf
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Logical Particle Filtering
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Dagstuhl Seminar Proceedings, Volume 7161
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2008-03-06
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Luke S. Zettlemoyer, Hanna M. Pasula, and Leslie Pack Kaelbling. Logical Particle Filtering. In Probabilistic, Logical and Relational Learning - A Further Synthesis. Dagstuhl Seminar Proceedings, Volume 7161, pp. 1-14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)
https://doi.org/10.4230/DagSemProc.07161.5
https://doi.org/10.4230/DagSemProc.07161.5
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.
Keywords
- Particle filter
- logical hidden Markov model
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