Subjective Logic: A Formalism for Reasoning Under Uncertainty

By David Bergman, A. Ciré, Willem-Jan van Hoeve, J. Hooker, Audun Jøsang. 2016

419 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Evidential Deep Learning to Quantify Classification Uncertainty (2018, methodology): “In this paper, we put our full focus on the uncertainty estimation problem and approach it from a Theory of Evidence perspective [7, 14].”
Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI (2020, positive): “This approach also benefits from a direct mapping to the framework of subjective logic.(49) Subjective logic hasmany appealing properties for AI applications in the coalition setting, allowing aleatoric and epistemic uncertainty to be considered during logical reasoning operations as well as”
Trusted Multi-View Classification With Dynamic Evidential Fusion (2022, methodology): “Therefore, the subjective logic [87] is introduced.”
Uncertainty Aware AI ML: Why and How (2018, background): “Evidential Deep Learning (EDL) aims to extend classical deep learning with the ideas from evidential reasoning and subjective logic (Dempster 2008; Jøsang 2016) to quantify uncertainty in classiﬁcation tasks (Sensoy, Kandemir, and Kaplan 2018).”
Uncertainty-Aware Deep Classifiers Using Generative Models (2020, background): “10 , . . . , 1 (cid:4) ) , which indicates that 8 more pieces of evidence is observed for the assignment of the sample to the ﬁrst class (Josang 2016).”