Advertisement

Comparison of Hard and Probabilistic Evidence in Bayesian Model

  • Rebai RimEmail author
  • Maalej Mohamed Amin
  • Mahfoudhi Adel
  • Abid Mohamed
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 557)

Abstract

Bayesian networks are powerful tools for probabilistic reasoning with uncertain evidences. Evidence originates from information based on the variables of observation. In this paper, we focus on two types of evidences: hard evidence and probabilistic evidence. We were interested in updating an evidence represented by a Bayesian model. This paper presents the application of probabilistic evidence in an adaptive user interface. Then, we compare the Bayesian model using probabilistic evidence with the Bayesian model using hard evidence.

Keywords

Bayesian networks Probabilistic evidence Adaptive user interface 

References

  1. 1.
    Ben Mrad, A., Delcroix, V., Piechowiak, S., Leicester, P., Abid, M.: An explication of uncertain evidence in Bayesian networks: likelihood evidence and probabilistic evidence. Appl. Intell. 1–23 (2015)Google Scholar
  2. 2.
    Bloemeke, M.: Agent encapsulated Bayesian networks. Ph.D. thesis, Department of Computer Science, University of South Carolina (1998)Google Scholar
  3. 3.
    Brusilovsky, P., Millán, E.: User models for adaptive hypermedia and adaptive educational systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 3–53. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-72079-9_1 CrossRefGoogle Scholar
  4. 4.
    Chan, H., Darwiche, A.: On the revision of probabilistic beliefs using uncertain evidence. Artif. Intell. 163(1), 67–90 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Conati, C., Gertner, A., Vanlehn, K.: Using Bayesian networks to manage uncertainty in student modeling. J. User Model. User-Adap. Inter. 12(4), 371–417 (2002)CrossRefzbMATHGoogle Scholar
  6. 6.
    Darwiche, A.: Modeling and Reasoning with Bayesian Networks. Cambridge University Press, Cambridge (2009)CrossRefzbMATHGoogle Scholar
  7. 7.
    Fischer, G.: User modeling in human-computer interaction. J. User Model. User-Adap. Inter. 11, 65–86 (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    In-Jee, S., Sung-Bae, C.: Bayesian and behavior networks for context-adaptive user interface in a ubiquitous home environment. Int. J. Expert Syst. Appl. 40, 1827–1838 (2013)CrossRefGoogle Scholar
  9. 9.
    Jeffrey, R.C.: The Logic of Decision, 2nd edn., p. 246. University of Chicago Press, Chicago (1990)Google Scholar
  10. 10.
    Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  11. 11.
    Korb, K., Nicholson, A.: Bayesian Artificial Intelligence. Chapman and Hall/CRC Computer Science and Data Analysis, Boca Raton (2010)zbMATHGoogle Scholar
  12. 12.
    Korpipaa, P., Koskinen, M., Peltola, J., Makela, S.M., Seppanen, T.: Bayesian approach to sensor-based context awareness. Pers. Ubiquit. Comput. 7(2), 113124 (2003)CrossRefGoogle Scholar
  13. 13.
    Koski, T., Noble, J.: Bayesian Networks: An Introduction. Wiley Series in Probability and Statistics. Wiley, Hoboken (2009)CrossRefzbMATHGoogle Scholar
  14. 14.
    Naim, P., Wuillemin, P.H., Leray, P., Pourret, O., Becker, A.: Rseaux Baysiens (2007)Google Scholar
  15. 15.
    Nguyen, L., Do, P.: Combination of Bayesian network and overlay model in user modeling. In: Allen, G., Nabrzyski, J., Seidel, E., Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2009. LNCS, vol. 5545, pp. 5–14. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-01973-9_2 CrossRefGoogle Scholar
  16. 16.
    Pan, R., Peng, Y., Ding, Z.: Belief update in Bayesian networks using uncertain evidence. In: ICTAI, pp. 441–444 (2006)Google Scholar
  17. 17.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems - Networks of Plausible Inference. Morgan Kaufman, San Mateo (1988)zbMATHGoogle Scholar
  18. 18.
    Peng, Y., Zhang, S., Pan, R.: Bayesian network reasoning with uncertain evidences. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 18(5), 539–564 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Peng, Y., Ding, Z., Zhang, S., Pan, R.: Bayesian network revision with probabilistic constraints. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 20(3), 317–337 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Rim, R., Mohamed Amin, M., Adel, M.: Bayesian networks for user modeling: predicting the user’s preferences. In: International Conference on Hybrid Intelligent Systems, HIS 2013, pp. 144–148 (2013)Google Scholar
  21. 21.
    Di Tomaso, E., Baldwin, J.F.: An approach to hybrid probabilistic models. Int. J. Approx. Reason. 47(2), 202–218 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Valtorta, M., Kim, Y.G., Vomlel, J.: Soft evidential update for probabilistic multiagent systems. Int. J. Approx. Reason. 29(1), 71–106 (2002)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Rebai Rim
    • 1
    Email author
  • Maalej Mohamed Amin
    • 1
  • Mahfoudhi Adel
    • 1
  • Abid Mohamed
    • 1
  1. 1.ENIS, Embedded Computer System LabUniversity of SfaxSfaxTunisia

Personalised recommendations