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state-estimationcausal
2017
Stefano V. Albrecht, Subramanian Ramamoorthy
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)
International Joint Conference on Artificial Intelligence, 2017
Abstract | BibTex | arXiv
IJCAIstate-estimationcausal
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.
@inproceedings{ albrecht2017causality,
title = {Exploiting Causality for Selective Belief Filtering in Dynamic {B}ayesian Networks (Extended Abstract)},
author = {Stefano V. Albrecht and Subramanian Ramamoorthy},
booktitle = {Proceedings of the 26th International Joint Conference on Artificial Intelligence},
address = {Melbourne, Australia},
month = {August},
year = {2017}
}
2016
Stefano V. Albrecht, Subramanian Ramamoorthy
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks
Journal of Artificial Intelligence Research, 2016
Abstract | BibTex | arXiv | Publisher
JAIRstate-estimationcausal
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 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.
@article{ albrecht2016causality,
title = {Exploiting Causality for Selective Belief Filtering in Dynamic {B}ayesian Networks},
author = {Stefano V. Albrecht and Subramanian Ramamoorthy},
journal = {Journal of Artificial Intelligence Research},
volume = {55},
pages = {1135--1178},
year = {2016},
publisher = {AI Access Foundation},
note = {DOI: 10.1613/jair.5044}
}