Improving the Behavior of Intelligent Tutoring Agents with Data Mining

Authors:
Roger Nkambou;Philippe Fournier-Viger;Engelbert Mephu Nguifo
Affiliations:
University of Quebec;University of Quebec;Université Blaise- Pascal, Clermont-Ferrand II
Venue:
IEEE Intelligent Systems
Year:
2009

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Abstract

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.