  Dynamic Bayesian networks (DBNs) are a general model for stochastic processes
with partially observed states. Belief filtering in DBNs is the task of
inferring the belief state (i.e. the probability distribution over process
states) based on incomplete and uncertain observations. In this article, we
explore the idea of accelerating the filtering task by automatically exploiting
causality in the process. We consider a specific type of causal relation,
called passivity, which pertains to how state variables cause changes in other
variables. We present the Passivity-based Selective Belief Filtering (PSBF)
method, which maintains a factored belief representation and exploits passivity
to perform selective updates over the belief factors. PSBF is evaluated in both
synthetic processes and a simulated multi-robot warehouse, where it
outperformed alternative filtering methods by exploiting passivity.
