{"type":"ScholarlyArticle","title":"Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)","authors":[{"name":"Stefano V. Albrecht","slug":"stefano-v-albrecht","given":"Stefano V.","family":"Albrecht","affiliation":"The University of Texas at Austin Austin , Texas , United States"},{"name":"Subramanian Ramamoorthy","slug":"subramanian-ramamoorthy","given":"Subramanian","family":"Ramamoorthy","affiliation":"The University of Edinburgh Edinburgh , United Kingdom"}],"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.","doi":"10.24963/ijcai.2017/727","url":"https://ijcai.org/proceedings/2017/727","pdf":"https://ijcai.org/proceedings/2017/0727.pdf","markdown":"https://ijcai.org/proceedings/2017/727.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/727","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Journal track","track":"Journal track","number":727,"pages":{"first":5085,"last":5089},"keywords":["Uncertainty in AI: Bayesian Networks","Planning and Scheduling: POMDPs","Knowledge Representation, Reasoning, and Logic: Belief Change","Uncertainty in AI: Approximate Probabilistic Inference"],"videos":[],"references":[{"position":1,"raw":"[Albrecht and Ramamoorthy, 2016] Stefano V. 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