Abstract
Since its introduction in the 1960s, Dempster-Shafer theory became one of the leading strands of research in artificial intelligence with a wide range of applications in business, finance, engineering and medical diagnosis. In this paper, we aim to grasp the essence of Dempster-Shafer theory by distinguishing between ambiguous-and-questionable and ambiguous-but-correct perceptions. Throughout the paper, we reflect our analysis in terms of signals and sensors as a natural field of application. We model ambiguous-and-questionable perceptions as a probability space with a quantity random variable and an additional perception random variable (Dempster model). We introduce a correctness property for perceptions. We use this property as an axiom for ambiguous-but-correct perceptions. In our axiomatization, Dempster’s lower and upper probabilities do not have to be postulated: they are consequences of the perception correctness property. Even more, we outline how Dempster’s lower and upper probabilities can be understood as best possible estimates of quantity probabilities. Finally, we define a natural knowledge fusion operator for perceptions and compare it with Dempster’s rule of combination.
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Notes
- 1.
Note, that standard the notation for random variables applies throughout all the paper, i.e., given a random variables \(X : \omega \longrightarrow I\), we have that Expr(X) denotes the event \(\{ \omega \, | \, Expr(X(\omega )) \}\) for each common mathematical expression \(Expr(\_ {\!}\_)\). For example, \((X=y)\) stands for \(X^{-1}(y)=\{ \omega \, | X(\omega )=y \}\) as usual; \(X \subseteq A\) stands for \(\{ \omega \, | X(\omega ) \subseteq A \}\); \(A \subset X\) stands for \(\{ \omega \, | A \subset X(\omega )\}\) etc.
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Draheim, D., Tammet, T. (2020). From Sensors to Dempster-Shafer Theory and Back: The Axiom of Ambiguous Sensor Correctness and Its Applications. In: Hartmann, S., Küng, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2020. Lecture Notes in Computer Science(), vol 12391. Springer, Cham. https://doi.org/10.1007/978-3-030-59003-1_1
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