Logical Particle Filtering

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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. Consider an agent operating in a complex environment, made up of an unknown, possibly infinite, number of objects. The agent can take actions and make observations of the state of the world, and it knows a probabilistic model of how the state changes over time as a result of its actions and of how the observations are generated from the states. How can it efficiently estimate the underlying state of the environment? Filtering is the problem of predicting a distri...
Luke S. Zettlemoyer, Hanna M. Pasula, Leslie Pack
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Authors Luke S. Zettlemoyer, Hanna M. Pasula, Leslie Pack Kaelbling
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