Abstract
In this paper, we propose to build agents that learn by observing other agents performing a task by extracting frequent temporal patterns from their behavior. We propose a learning mechanism consisting of three phases: (1) recording other agents’ behavior, (2) mining temporal patterns from this data and (3) utilizing the resulting knowledge. We illustrate this approach with a tutoring system for training learners to robotized arm manipulation where we have integrated a tutoring agent that observes humans performing a task to learn it. The agent then exploits this knowledge to provide assistance to learners.
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Fournier-Viger, P., Nkambou, R., Mephu Nguifo, E., Faghihi, U. (2009). Building Agents That Learn by Observing Other Agents Performing a Task: A Sequential Pattern Mining Approach. In: Chien, BC., Hong, TP. (eds) Opportunities and Challenges for Next-Generation Applied Intelligence. Studies in Computational Intelligence, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92814-0_43
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DOI: https://doi.org/10.1007/978-3-540-92814-0_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92813-3
Online ISBN: 978-3-540-92814-0
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