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

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