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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Designing Possibilistic Information Fusion Systems: Redundancy as Criterion for Fusion Topologies

Designing Possibilistic Information Fusion Systems: Redundancy as Criterion for Fusion Topologies

Holst, Christoph-Alexander

Paperback

Series: Technologien Für Die Intelligente Automation, Book 20

General ComputersProbability & Statistics

Currently unavailable to order

ISBN10: 3032106958
ISBN13: 9783032106957
Publisher: Springer
Published: Jan 3 2026
Pages: 221
Weight: 1.06
Height: 0.49 Width: 6.67 Depth: 9.56
Language: English

Intelligent technical systems process information from multiple sources, but are confronted with uncertainties inherent in the information which is often imprecise, incomplete, or inconsistent. As the number of information sources increases, so does the uncertainty, as well as the risk that individual sources are unreliable. This leads to a lack of confidence in analyses and decisions. This thesis presents the Redundancy-hardened Robust Fusion System (R2FS), which aims to exploit redundancies in information sources to increase robustness against changes in source reliability. Leveraging the strengths of possibility theory, it identifies redundancies in information sources, even in environments where information is scarce and characterised by a high degree of epistemic uncertainty. Based on the novel dual redundancy metric proposed in this thesis, redundant sources are aligned in a distributed fusion topology. It is demonstrated that the R2FS outperforms established possibilistic fusion rules in terms of robustness due to the exploitation of redundancy in the distributed topology. This book concludes with a discussion of the current state of uncertainty modelling, highlighting how uncertainty modelling techniques currently used in information fusion could benefit machine learning applications.

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