# Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)

## TL;DR

This paper introduces PSBF, a novel belief filtering method for Dynamic Bayesian Networks that leverages causality, specifically passivity, to improve efficiency and performance in stochastic process inference.

## Contribution

The paper proposes a passivity-based selective belief filtering approach that exploits causal relations to accelerate inference in DBNs, demonstrated in synthetic and robotic scenarios.

## Key findings

- PSBF outperforms traditional filtering methods in experiments.
- Exploiting passivity reduces computational complexity.
- Effective in multi-robot warehouse simulation.

## Abstract

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.