• Data Mining Algorithms with Applications in Tutoring Agents
• Evaluating Spatial Reasoning in Intelligent Tutoring Systems
• A Cognitive and Ontology-Based Model for Building Glass-Box Learning Objects
• A Cognitive Model for Building "Cognitive Tutors"
In my recent works, I have developped data mining algorithms to enhance the capabilities of intelligent agents. In particular, I have explored the possibility of improving the behavior of agents by discovering temporal patterns in their behavior.
For this purpose, I have devopped an original sequential pattern mining (SPM) algorithm providing several more features than classical SPM algorithms such as handling time constraints, valued items, dimensional information and eliminating some form of redundancy .
This algorithm has been applied in two tutoring agents. First, we have build an agent that can learn a task by observation. It proceeds by recording the behavior of other agents and then extracts frequent patterns from this data [1, 2]. In our application, this allowed the tutoring agent to learn domain knowledge by observing humans. The agent can then use this knowledge to provide tailored assistance to learners. Second, we have built an agent that learn from its own behavior by reusing behaviors that lead to self-satisfaction [2, 3]. This allow an author to provide the agent with different behaviors and to let the agent learn automatically which one is more succesful in different situations.
Other works with data mining and agents are currently in progress.
 Fournier-Viger, P., Nkambou, R & Mephu Nguifo, E. (2008), A Knowledge Discovery Framework for Learning Task Models from User Interactions in Intelligent Tutoring Systems. Proceedings of the 7th Mexican International Conference on Artificial Intelligence (MICAI 2008). LNAI 5317, Springer, pp. 765-778.
 Fournier-Viger, P., Nkambou, R., Faghihi, U. & Mephu Nguifo, E. (2009). Mining Temporal Patterns to Improve Agents Behavior: Two Case Studies. In Cao, L.(Ed.) Data Mining and Multiagent Integration, Springer, 15 pages (to appear).
 Faghihi, U., Fournier-Viger, P., Nkambou, R. & Poirier, P. (2009). A Generic Episodic Learning Model Implemented in a Cognitive Agent by Means of Temporal Pattern Mining. Proceedings of the 22nd Intern.Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IAE-AIE 2009), LNAI 5579, Springer, pp. 545-555.
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