Ascribing Causality from Interventional Belief Function Knowledge

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)

Abstract

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.

Keywords

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.

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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

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