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Ant Colony Models for a Virtual Educational Environment Based on a Multi-Agent System

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Large-Scale Scientific Computing (LSSC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4818))

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Abstract

We have designed a virtual learning environment where students interact through their computers and with the software agents in order to achieve a common educational goal. The Multi-Agent System (MAS) consisting of autonomous, cognitive and social agents communicating by messages is used to provide a group decision support system for the learning environment. Learning objects are distributed in a network and have different weights in function of their relevance to a specific educational goal. The relevance of a learning object can change in time; it is affected by students’, agents’ and teachers’ evaluation. We have used an ant colony behavior model for the agents that play the role of a tutor and organizing the group-work activities for the students.

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References

  1. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence: From natural to artificial systems. In: Santa Fe Institute Studies of Complexity, Oxford University Press, Oxford (1999)

    Google Scholar 

  2. Bonabeau, E., Dorigo, M., Theraulaz, G.: Inspiration for optimization from social insect behaviour. Nature 406, 39–42 (2000)

    Article  Google Scholar 

  3. Deneubourg, J.L., et al.: The Self-Organizing Exploratory Pattern of the Argentine Ant. Journal of Insect Behaviour 3, 159–168 (1990)

    Article  Google Scholar 

  4. Dorigo, M.: Optimization, Learning and Natural Algorithms, Ph.D. Thesis, Politecnico di Milano, Italy (1992)

    Google Scholar 

  5. Dorigo, M., Birattari, M., Stützle, T.: Ant Colony Optimization, Artificial Ants as a Computational Intelligence Technique. IEEE Computational Intelligence Magazine (2006)

    Google Scholar 

  6. Dorigo, M., Birattari, M., Stützle, T.: Ant Colony Optimization, Artificial Ants as a Computational Intelligence Technique, IRIDIA — Technical Report Series, Technical Report No.TR/IRIDIA/2006-023 (2006)

    Google Scholar 

  7. Dorigo, M., Di Caro, G.: The Ant Colony Optimization Meta-Heuristic. In: Crone, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 11–32. McGraw-Hill, New York (1999)

    Google Scholar 

  8. Dorigo, M., Gambardella, L.M.: Ant Colonies for the Traveling Salesman Problem. BioSystems 43, 73–81 (1997)

    Article  Google Scholar 

  9. Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In: FOGA 1991, vol. 1, pp. 69–93 (1991)

    Google Scholar 

  10. Goss, S., et al.: Self-Organized Shortcuts in the Argentine Ant. Naturwissenschaften 76, 579–581 (1989)

    Article  Google Scholar 

  11. Jennings, N.R., Wooldridge, M.: Applications of Intelligent Agents. In: Jennings, N., Wooldridge, M. (eds.) Agent Technology: Foundations, Applications, and Markets, Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  12. Lenstra, J.K., Rinnooy Kan, A.H.G.: Optimization and Approximation in Deterministic Sequencing and Scheduling: A Survey. Annals of Discrete Mathematics 5, 287–326 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  13. Moisil, I., et al.: Socio-cultural modelling of student as the main actor of a virtual learning environment. WSEAS Transaction on Information Science and Applications 4, 30–36 (2007)

    Google Scholar 

  14. Nouyan, Sh., Dorigo, M.: Path Formation in a Robot Swarm, IRIDIA — Technical Report Series, Technical Report No.TR/IRIDIA/2007-002 (2007)

    Google Scholar 

  15. Resnick, M.: Turtles, Termites and Traffic Jams. Explorations in Massively Parallel Microworlds. In: Complex Adaptive Systems, MIT Press, Cambridge (1994)

    Google Scholar 

  16. Stützle, T., Dorigo, M.: ACO Algorithms for the Travelling Salesman Problem. In: Makela, M., et al. (eds.) Proceedings of the EUROGEN conference, pp. 163–184. John Wiley & Sons, Chichester (1999)

    Google Scholar 

  17. Tsui, K.C., Liu, J.: Multiagent Diffusion and Distributed Optimization. In: Proceedings of the Second International Joint Conference on Autonomous Agents & Multiagent Systems, pp. 169–176. ACM, New York (2003)

    Chapter  Google Scholar 

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© 2008 Springer-Verlag Berlin Heidelberg

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Moisil, I., Pah, I., Simian, D., Simian, C. (2008). Ant Colony Models for a Virtual Educational Environment Based on a Multi-Agent System. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2007. Lecture Notes in Computer Science, vol 4818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78827-0_66

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  • DOI: https://doi.org/10.1007/978-3-540-78827-0_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78825-6

  • Online ISBN: 978-3-540-78827-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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