Logical Particle Filtering
Luke Zettlemoyer, Hanna Pasula, Leslie Pack Kaelbling · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2008
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.