Proposition of an Intelligent Multi-Agent System for Learning
In this paper, an intelligent Multi-Agent System (MAS) for learning, that we have called MASIL, is proposed. MASIL’s architecture is based on the hybrid client-server, multi-agent and Machine Learning model. After creating the different agents of the system and defining the different roles, we formed groups of agents according to our architecture to make our system more efficient. After this step, we had to find an operating mechanism to make MASIL as efficient as possible. To try to solve this problem we used the approach of the Operating System kernel and made some connections with the MASIL agents. To make MASIL smarter, we built machine learning models for certain of our agents. The evaluation of the system has shown that MASIL is being formed as lessons are being learned and is becoming increasingly intelligent
2. Mazyad H. Une Approche Multi-agents à Architecture P2P pour l’Apprentissage Collaboratif [Ph.D. Thesis]. Université du Littoral côte d’opale, 31st January 2013.
3. Zidani A, Djoudi M, Zidat S et al. Chelia: Un environnement coopératif pour le l’apprentissage sur Internet. 6eme Colloque CARI 2002 de l’INRIA, ISBN: 2-7261-1214-5, Yaoundé, Cameroun, 14-17 Octobre, 339-346.
4. Ganga T. Un modèle multi-agent de la communication entre ruches d’abeilles dans un système fermé [Master’s thesis]. Department of Mathematics and Computer Science, University of Ngaoundéré, 2012.
5. Ferber J. Les systèmes multi-agents : Vers une intelligence collective. InterEditions, 1995.
6. Russell. Rationality and intelligence. Artificial Intelligence 1997; 94: 57-77.
7. Phil S. Too Big to Ignore: The Business Case for Big Data. Wiley, March 18, 2013; 89. ISBN 978-1-118-63817-0.
8. Mitchell T. Machine Learning. McGraw Hill, 1997: pp-2. ISBN 0-07-042807-7.