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Adaptive e-Learning Multi-Agent Systems with Swarm Intelligence

Manuj Darbari, Priya Sahai Published in Artificial Intelligence

Adaptive e-Learning Multi-Agent Systems with Swarm Intelligence
International Journal of Applied Information Systems
Year of Publication: 2014
© 2013 by IJAIS Journal
Series Volume 7 Number 3
Authors Manuj Darbari, Priya Sahai
10.5120/ijais14-451164
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  1. Manuj Darbari and Priya Sahai. Article: Adaptive e-Learning Multi-Agent Systems with Swarm Intelligence. International Journal of Applied Information Systems 7(3):16-20, May 2014. BibTeX

    @article{key:article,
	author = "Manuj Darbari and Priya Sahai",
	title = "Article: Adaptive e-Learning Multi-Agent Systems with Swarm Intelligence",
	journal = "International Journal of Applied Information Systems",
	year = 2014,
	volume = 7,
	number = 3,
	pages = "16-20",
	month = "May",
	note = "Published by Foundation of Computer Science, New York, USA"
}

Abstract

In this paper we propose a multi-agent approach to the problem of recommending relevant and adequate study material to the learner in E-learning environment. It illustrates swarm intelligence of bee-colony optimization that enables agents to recommend learner most appropriate data content in real –time which stored in the form of case-sets. A flexibility, adaptability and interactiveness is achieved through agents that autonomously and intelligently uses swarm intelligent algorithms to recommend course structure to learners . Secondly we have suggested construction of a cache knowledge base which will be updated by swarm intelligent agents by analysing various parameters such as learners feedbacks, their educational records and other parameters.

Reference

  1. A. P. Englebrecht, Fundamentals of computational swarm intelligence, Wiley, 2005.
  2. S. Ilie, C. B?adic?a, Multi-agent approach to distributed ant colony optimization, Science of Computer Programming.
  3. Sorin Iliea, Costin B?adic? aa, 2003. Multi-agent distributed framework for swarm intelligence,International Conference on Computational Science, ICCS.
  4. Priya Sahai, Manuj Darbari,,2014 . Adaptive e-learning using Granulerised Agent Framework, international Journal of Scientific & Engineering Research, Volume 5, Issue 2, February,ISSN 2229-5518.
  5. Task allocation in Case-based Recommender Systems: A swarm intelligence approach by Fabiana Lorenzi, Daniela Scherer dos Santos, Denise de Oleviera and Ana L. C. Bazzan,2007.
  6. Mario F. Triola, Baye's Theorem, pearson education,2010.
  7. M Darbari, N Dhanda , Applying Constraints in Model Driven Knowledge Representation Framework,2010. International Journal of Hybrid Information Technology 3 (3)4.
  8. M Darbari, S Medhavi, AK Srivastava Development of effective Urban Road Traffic Management using workflow wechniques for upcoming metro cities like Lucknow (India Development 2 (2),4,2008.
  9. N Dhanda, M Darbari, NJ Ahuja,,2012 Development of Multi Agent Activity Theory e-Learning (MATeL) Framework Focusing on Indian Scenario International Review on Computers & Software 7 (4),2.
  10. A. Tiwari, P. Patel , V. K. Singh , A. Srivastava. Implementing Requirements for the hospital management system using multi- agent.

Keywords

E-learning, swarm intelligence , multi-agent system, cache, Baye’s theorem