Ascribing Causality from Interventional Belief Function Knowledge

  • Imen Boukhris
  • Salem Benferhat
  • Zied Elouedi
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)


In many Artificial Intelligence applications, causality is an important issue. Interventions are external manipulations that alter the natural behavior of the system. They have been used as tools to distinguish causal relations from spurious correlations. This paper proposes a model allowing the detection of causal relationships under the belief function framework resulting from acting on some events. Facilitation and justification in the presence of interventions, concepts complementary to the concept of causality, are also discussed in this paper.


Intrusion Detection System Ascribe Causality Belief Function Abnormal Event Causal Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Benferhat, S.: Interventions and belief change in possibilistic graphical models. Artif. Intell. 174(2), 177–189 (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Benferhat, S., Bonnefon, J.-F., Chassy, P., Da Silva Neves, R., Dubois, D., Dupin de Saint-Cyr, F., Kayser, D., Nouioua, F., Nouioua-Boutouhami, S., Prade, H., Smaoui, S.: A Comparative Study of Six Formal Models of Causal Ascription. In: Greco, S., Lukasiewicz, T. (eds.) SUM 2008. LNCS (LNAI), vol. 5291, pp. 47–62. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Benferhat, S., Smaoui, S.: Possibilistic causal networks for handling interventions: A new propagation algorithm. In: AAAI, pp. 373–378. AAAI Press (2007)Google Scholar
  4. 4.
    Benferhat, S., Smaoui, S.: Quantitative Possibilistic Networks: Handling Interventions and Ascribing Causality. In: Gelbukh, A., Morales, E.F. (eds.) MICAI 2008. LNCS (LNAI), vol. 5317, pp. 720–731. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Benferhat, S., Smaoui, S.: Inferring interventions in product-based possibilistic causal networks. Fuzzy Sets and Systems 169(1), 26–50 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Bonnefon, J., Da Silva Neves, R., Dubois, D., Prade, H.: Background default knowledge and causality ascriptions. In: ECAI, pp. 11–15 (2006)Google Scholar
  7. 7.
    Bonnefon, J.F., Da Silva Neves, R., Dubois, D., Prade, H.: Predicting causality ascriptions from background knowledge: model and experimental validation. Int. J. Approx. Reasoning 48(3), 752–765 (2008)zbMATHCrossRefGoogle Scholar
  8. 8.
    Boukhris, I., Benferhat, S., Elouedi, Z.: A belief function model for ascribing causality. In: EPIA, pp. 342–356 (2011)Google Scholar
  9. 9.
    Boukhris, I., Elouedi, Z., Benferhat, S.: Modeling interventions using belief causal networks. In: FLAIRS, pp. 602–607 (2011)Google Scholar
  10. 10.
    Goldszmidt, M., Pearl, J.: Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions. In: KR, pp. 661–672 (1992)Google Scholar
  11. 11.
    Halpern, J., Pearl, J.: Causes and explanations: A structurel model approach. In: UAI, pp. 194–202 (2001)Google Scholar
  12. 12.
    Morin, B., Mé, L., Debar, H., Ducassé, M.: A logic-based model to support alert correlation in intrusion detection. Information Fusion 10(4), 285–299 (2009)CrossRefGoogle Scholar
  13. 13.
    Pearl, J.: Causality: Models, Reasonning and Inference. Cambridge University Press (2000)Google Scholar
  14. 14.
    Shafer, G.: A mathematical theory of evidence. Princeton University Press (1976)Google Scholar
  15. 15.
    Shafer, G.: The Art of Causal Conjecture. The MIT Press (1997)Google Scholar
  16. 16.
    Smets, P.: The combination of evidence in the transferable belief model. IEEE Pattern Analysis and Machine Intelligence 12(5), 447–458 (1990)CrossRefGoogle Scholar
  17. 17.
    Smets, P.: About updating. In: UAI, pp. 378–385 (1991)Google Scholar
  18. 18.
    Smets, P.: The transferable belief model for quantified belief representation, vol. 1, pp. 267–301. Kluwer Academic Publisher (1998)Google Scholar
  19. 19.
    Spohn, W.: Ordinal conditional functions: a dynamic theory of epistemic states causation in decision. In: Belief Changes and Statistics, pp. 105–134 (1988)Google Scholar
  20. 20.
    Wakker, P.: Dempster belief functions are based on the principle of complete ignorance. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 8(3), 271–284 (2000)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.CRIL-Université d’Artois-Faculté Jean PerrinLensFrance
  2. 2.LARODEC-Université de Tunis-ISGTunisTunisie

Personalised recommendations