Research Article

Adaptive e-Learning Multi-Agent Systems with Swarm Intelligence

by  Manuj Darbari, Priya Sahai
journal cover
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Issue 3
Published: May 2014
Authors: Manuj Darbari, Priya Sahai
10.5120/ijais14-451164
PDF

Manuj Darbari, Priya Sahai . Adaptive e-Learning Multi-Agent Systems with Swarm Intelligence. International Journal of Applied Information Systems. 7, 3 (May 2014), 16-20. DOI=10.5120/ijais14-451164

                        @article{ 10.5120/ijais14-451164,
                        author  = { Manuj Darbari,Priya Sahai },
                        title   = { 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 },
                        doi     = { 10.5120/ijais14-451164 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2014
                        %A Manuj Darbari
                        %A Priya Sahai
                        %T Adaptive e-Learning Multi-Agent Systems with Swarm Intelligence%T 
                        %J International Journal of Applied Information Systems
                        %V 7
                        %N 3
                        %P 16-20
                        %R 10.5120/ijais14-451164
                        %I Foundation of Computer Science (FCS), NY, 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.

References
  • A. P. Englebrecht, Fundamentals of computational swarm intelligence, Wiley, 2005.
  • S. Ilie, C. B?adic?a, Multi-agent approach to distributed ant colony optimization, Science of Computer Programming.
  • Sorin Iliea, Costin B?adic? aa, 2003. Multi-agent distributed framework for swarm intelligence,International Conference on Computational Science, ICCS.
  • 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.
  • 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.
  • Mario F. Triola, Baye's Theorem, pearson education,2010.
  • M Darbari, N Dhanda , Applying Constraints in Model Driven Knowledge Representation Framework,2010. International Journal of Hybrid Information Technology 3 (3)4.
  • 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.
  • 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.
  • A. Tiwari, P. Patel , V. K. Singh , A. Srivastava. Implementing Requirements for the hospital management system using multi- agent.
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

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

Powered by PhDFocusTM