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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 Passivitybased 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.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)
PublisherIJCAI Inc
Pages5085-5089
Number of pages5
ISBN (Electronic)978-0-9992411-0-3
Publication statusPublished - 25 Aug 2017
Event26th International Joint Conference on Artificial Intelligence - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017
https://ijcai-17.org/index.html
https://ijcai-17.org/
https://ijcai-17.org/

Conference

Conference26th International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI 2017
CountryAustralia
CityMelbourne
Period19/08/1725/08/17
Internet address

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