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Experimenting with a real-size man-hill to optimize pedagogical paths

Published:13 March 2005Publication History

ABSTRACT

This paper describes experiments aimed at adapting Ant Colony Optimization (ACO) techniques to an e-learning environment, thanks to the fact that the available on-line material can be organized in a graph by means of hyperlinks between educational topics. The structure of this graph is to be optimized in order to facilitate the learning process for students.ACO is based on an ant-hill metaphor. In this case, however, the agents that move on the graph are students who unconsciously leave pheromones in the environment depending on their success or failure. In the paper, the whole process is therefore referred to as a "man-hill."Compared to the [13, 14] papers that were providing guidelines for this problem, real-size tests have been performed, showing that man-hills behave differently from ant-hills. The notion of pheromone erosion (rather than evaporation) is introduced.

References

  1. E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: "From natural to Artificial systems, Oxford University Press 1999, ISBN 0-19-513159-2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. E. Bonabeau, M. Dorigo and G. Theraulaz, Inspiration for optimization from social insect behavior, in Nature, vol. 406 pp 39--42, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  3. A. Colorni, M. Dorigo and V. Maniezzo, Distributed optimization by ant-colonies, in proceedings of European Conference on Artificial Life, Cambridge, MIT Press, pp 134--142, 1991.Google ScholarGoogle Scholar
  4. J. L. Deneubourg, S. Aron, S. Goss and J. M. Pasteels, The self-organizing exploratory pattern of the argentine ant, in Journal of Insect Behavior, vol. 3 pp 159--168, 1990.Google ScholarGoogle Scholar
  5. M. Dorigo, E. Bonabeau and G. Theraulaz, Ant algorithms and stigmergy, in Future Generation Computer Systems, vol. 16 pp 851--871, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. M. Dorigo and G. Di Caro, The ant colony optimization metaheuristic, in New ideas in optimization, D. Corne, M. Dorigo and F. Glover (Eds), McGraw-Hill, pp 11--32, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Dorigo, Optimization, learning and natural algoritmhs, PhD Thesis, politecnico di Milano, 1992.Google ScholarGoogle Scholar
  8. J. L. Deneubourg, J. M. Pasteels and J. C. Verhaeghe, Probalistic behavior in ants: a strategy of errors?, in Theoritical Biology, vol. 105 pp 259--271, 1983.Google ScholarGoogle ScholarCross RefCross Ref
  9. D. E. Goldberg and K. Deb, A comparative analysis of selection schemes used in genetic algorithms, in G. Rawlins, editor, Foundations of Genetic Algorithms, vol. 1 pp 69--93, 1991.Google ScholarGoogle Scholar
  10. D. E. Goldberg, Genetic algorithms in search, optimization and machine learning, Addison-Wesley Publishing Company Inc., Reading, MA, 1989. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. N. Labroche, N. Monmarch and G. Venturini, A new clustering algorithm based on the chemical recognition system of ants, in proceedings of the European Conference on Artificial Intelligence, IOS Press, pp 345--349, 2002.Google ScholarGoogle Scholar
  12. M. Resnick, Turtles, termites and traffic jams: Explorations in massively parallel microworlds, Complex adaptive systems series MIT Press, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Y. Semet, Y. Jamont, R. Biojout, E. Lutton and P. Collet, Artificial Ant Colony and E-Learning: An optimization of pedagogical paths, HCI 2003.Google ScholarGoogle Scholar
  14. Y. Semet, E. Lutton and P. Collet, Ant Colony Optimization for E-Learning: Observing the emergence of pedagogic suggestions, SIS 2003.Google ScholarGoogle ScholarCross RefCross Ref
  15. T. Sttzle, M. Dorigo, ACO Algorithms for the travelling salesman problem, in proceedings of the EUROGEN conference, M. Maleka, K. Miettinen, P. Neittaanmaki, J. Periaux (Eds), John Wiley & Sons, pp 163--183, 1999.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Conferences
    SAC '05: Proceedings of the 2005 ACM symposium on Applied computing
    March 2005
    1814 pages
    ISBN:1581139640
    DOI:10.1145/1066677

    Copyright © 2005 ACM

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    New York, NY, United States

    Publication History

    • Published: 13 March 2005

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