Skip to main content

From Sensors to Dempster-Shafer Theory and Back: The Axiom of Ambiguous Sensor Correctness and Its Applications

Keynote at DEXA’2020 – The 31st International Conference on Database and Expert Systems Applications

  • Conference paper
  • First Online:
Database and Expert Systems Applications (DEXA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12391))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 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.

References

  1. Dempster, A.P.: New methods for reasoning towards posterior distributions based on sample data. Ann. Math. Stat. 37(2), 355–374 (1966)

    Article  MathSciNet  MATH  Google Scholar 

  2. Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38(2), 325–339 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  3. Dempster, A.P.: A generalization of Bayesian inference. Technical report AD 664 659, Harvard University, November 1967

    Google Scholar 

  4. Dempster, A.P.: A generalization of Bayesian inference. J. Roy. Stat. Soc. B 30(2), 205–247 (1968)

    MathSciNet  MATH  Google Scholar 

  5. Dempster, A.P.: Upper and lower probability inferences based on a sample from a finite univariate population. Biometrika 45(3), 515–528 (1967)

    Article  MathSciNet  Google Scholar 

  6. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  7. Liu, L., Yager, R.R.: Classic works of the Dempster-Shafer theory of belief functions: an introduction. In: Yager, R.R., Liu, L. (eds.) Classic Works of the Dempster-Shafer Theory of Belief Functions. STUDFUZZ, vol. 219, pp. 1–35. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-44792-4_1

    Chapter  MATH  Google Scholar 

  8. Wu, C., Barnes, D.: Formulating partner selection criteria for agile supply chains: a Dempster-Shafer belief acceptability optimisation approach. Int. J. Prod. Econ. 125(2), 284–293 (2010)

    Article  Google Scholar 

  9. Müller, J., Piché, R.: Mixture surrogate models based on Dempster-Shafer theory for global optimization problems. J. Global Optim. 51(1), 79–104 (2011). https://doi.org/10.1007/s10898-010-9620-y

    Article  MathSciNet  MATH  Google Scholar 

  10. Xiao, Z., Yang, X., Pang, Y., Dang, X.: The prediction for listed companies’ financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory. Knowl. Based Syst. 26, 196–206 (2012)

    Article  Google Scholar 

  11. Sevastianov, P., Dymova, L.: Synthesis of fuzzy logic and Dempster-Shafer theory for the simulation of the decision-making process in stock trading systems. Math. Comput. Simul. 80(3), 506–521 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  12. Hong, S., Lim, W., Cheong, T., May, G.: Fault detection and classification in plasma etch equipment for semiconductor manufacturing e-diagnostics. IEEE Trans. Semicond. Manuf. 25(1), 83–93 (2012)

    Article  Google Scholar 

  13. Lonea, A., Popescu, D., Tianfield, H.: Detecting DDoS attacks in cloud computing environment. Int. J. Comput. Commun. Control 8(1), 70–78 (2013)

    Article  Google Scholar 

  14. Straszecka, E.: Combining uncertainty and imprecision in models of medical diagnosis. Inf. Sci. 176(20), 3026–3059 (2006)

    Article  MathSciNet  Google Scholar 

  15. Bloch, I.: Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account. Pattern Recogn. Lett. 17(8), 905–919 (1996)

    Article  Google Scholar 

  16. Murphy, R.R.: Dempster-Shafer theory for sensor fusion in autonomous mobile robots. IEEE Trans. Robot. Autom. 14(2), 197–206 (1998)

    Article  Google Scholar 

  17. Wu, H., Siegel, M., Stiefelhagen, R., Yang, J.: Sensor fusion using Dempster-Shafer theory. In: Proceedings of IMTC 2002 - The 19th IEEE Instrumentation and Measurement Technology Conference, vol. 1, pp. 7–12. IEEE (2002)

    Google Scholar 

  18. Basir, O., Yuan, X.: Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory. Inf. Fusion 8(4), 379–386 (2007)

    Article  Google Scholar 

  19. Dempster, A.P.: Foreward. In: Shafer, G. (ed.) A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    Google Scholar 

  20. Carnap, R.: The two concepts of probability – the problem of probability. Philos. Phenomenol. Res. 5(4), 513–532 (1945)

    Article  MathSciNet  MATH  Google Scholar 

  21. Carnap, R.: On inductive logic. Philos. Sci. 12(2), 72–97 (1945)

    Article  MathSciNet  MATH  Google Scholar 

  22. Draheim, D.: Generalized Jeffrey Conditionalization – A Frequentist Semantics of Partial Conditionalization (with a Foreword by Bruno Buchberger). Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69868-7

    Book  MATH  Google Scholar 

  23. Draheim, D.: Semantics of the Probabilistic Typed Lambda Calculus – Markov Chain Semantics, Termination Behavior, and Denotational Semantics. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-642-55198-7

    Book  MATH  Google Scholar 

  24. Neyman, J.: Frequentist probability and frequentist statistics. Synthese 36, 97–131 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  25. Smets, P.: Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. In: Yager, R.R., Liu, L. (eds.) Classic Works of the Dempster-Shafer Theory of Belief Functions. Studies in Fuzziness and Soft Computing, vol. 219, pp. 633–664. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-44792-4_25

    Chapter  Google Scholar 

  26. Kolmogorov, A.: Grundbegriffe der Wahrscheinlichkeitsrechnung. Springer, Heidelberg (1933). https://doi.org/10.1007/978-3-642-49888-6

    Book  MATH  Google Scholar 

  27. Kolmogorov, A.: Foundations of the Theory of Probability. Chelsea, New York (1956)

    MATH  Google Scholar 

  28. Kolmogorov, A.: On logical foundation of probability theory. In: Itô, K., Prokhorov, J.V. (eds.) Probability Theory and Mathematical Statistics. Lecture Notes in Mathematics, vol. 1021, pp. 1–5. Springer, Dordrecht (1982). https://doi.org/10.1007/BFb0072897

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dirk Draheim .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59003-1_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59002-4

  • Online ISBN: 978-3-030-59003-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics