article

Evidentialist foundationalist argumentation for multi-agent sensor fusion

Authors:

Arvin AgahAuthors Info & Claims

Artificial Intelligence Review, Volume 42, Issue 2

Pages 211 - 243

https://doi.org/10.1007/s10462-012-9333-3

Published: 01 August 2014 Publication History

Abstract

In this paper, an approach to evidence-based argumentation called Evidentialist Foundationalist Argumentation (EFA) is formally defined in terms of the ASPIC framework. The EFA framework is then used as the basis for general argument patterns applied to the problem domain of Sensor Fusion. These general Sensor Fusion argument patterns serve as templates for concrete arguments constructed by agents in an in situ Sensor Web. These agents use EFA to solve specific instances of the Decentralized Sensor Fusion problem by strategically sharing evidence from their arguments using a Share on Disagreement protocol. Using real-world data, the performance of this multiagent system is compared to the performance of another multiagent system employing a Kalman Filtering approach. The results are statistically analyzed using omega-squared effect sizes produced by ANOVA with p values < 0.05. The EFA based system is found to outperform the Kalman Filtering system in terms of accuracy with mostly high and medium effect sizes. The Kalman Filtering system is found to outperform the EFA based system in terms of communication costs with mostly low effect sizes.

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Cited By

Liu Z(2023)An effective conflict management method based on belief similarity measure and entropy for multi-sensor data fusionArtificial Intelligence Review10.1007/s10462-023-10533-056:12(15495-15522)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10462-023-10533-0
Pan DWu Z(2021)Fault-Tolerant Consensus for Leader-following Multi-Agent Systems under False Data Injection AttacksProceedings of the 2021 13th International Conference on Machine Learning and Computing10.1145/3457682.3457721(258-264)Online publication date: 26-Feb-2021
https://dl.acm.org/doi/10.1145/3457682.3457721

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cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 42, Issue 2

August 2014

145 pages

ISSN:0269-2821

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Copyright © Copyright © 2014 Springer Science+Business Media Dordrecht.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 August 2014

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Cited By

Liu Z(2023)An effective conflict management method based on belief similarity measure and entropy for multi-sensor data fusionArtificial Intelligence Review10.1007/s10462-023-10533-056:12(15495-15522)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10462-023-10533-0
Pan DWu Z(2021)Fault-Tolerant Consensus for Leader-following Multi-Agent Systems under False Data Injection AttacksProceedings of the 2021 13th International Conference on Machine Learning and Computing10.1145/3457682.3457721(258-264)Online publication date: 26-Feb-2021
https://dl.acm.org/doi/10.1145/3457682.3457721

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