Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian
  Networks
  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 noisy observations. This can be a hard problem
in complex processes with large state spaces. 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 produces exact
belief states under certain assumptions and approximate belief states
otherwise, where the approximation error is bounded by the degree of
uncertainty in the process. We show empirically, in synthetic processes with
varying sizes and degrees of passivity, that PSBF is faster than several
alternative methods while achieving competitive accuracy. Furthermore, we
demonstrate how passivity occurs naturally in a complex system such as a
multi-robot warehouse, and how PSBF can exploit this to accelerate the
filtering task.
