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Issue No. 03 - May/June (2009 vol. 24)
ISSN: 1541-1672
pp: 46-53
Roger Nkambou , University of Quebec
Philippe Fournier-Viger , University of Quebec
Engelbert Mephu Nguifo , Université Blaise- Pascal, Clermont-Ferrand II
This article presents a novel framework for adapting the behavior of intelligent agents. The framework consists of an extended sequential pattern mining algorithm that, in combination with association rule discovery techniques, is used to extract temporal patterns and relationships from the behavior of human agents executing a procedural task. The proposed framework has been integrated within the CanadarmTutor, an intelligent tutoring agent aimed at helping students solve procedural problems that involve moving a robotic arm in a complex virtual environment. We present the results of an evaluation that demonstrates the benefits of this integration to agents acting in ill-defined domains.
data mining, intelligent agent, intelligent tutoring systems, cognitive agent, knowledge acquisition, knowledge discovery

E. M. Nguifo, P. Fournier-Viger and R. Nkambou, "Improving the Behavior of Intelligent Tutoring Agents with Data Mining," in IEEE Intelligent Systems, vol. 24, no. , pp. 46-53, 2009.
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