Advertisement

Intelligent Market Based Learner Modeling

  • Maryam Ashoori
  • Chun Yan Miao
  • Angela Eck Soong Goh
  • Wang Qiong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4099)

Abstract

This paper presents an economical inspired intelligent approach for modeling learners in learning systems. Decision making in complex systems like e-learning systems requires processing of large amounts of heterogeneous data and information from dispread sources. Moreover, most of the decision parameters are incomplete and uncertain. Lacking of a complete model of learner is the prominent problem of current learning systems. In this paper, a market based method for describing Learner’s preferences to the learning system is provided. The proposed approach strives for applying a Dempster-Shafer decision making over a society of self motivated agents. It tries to present a final learner agent with a high degree of similarity to the user for the purpose that it can act as a model of learner through the system. An implicit learning is also implemented by the idea of Stocks in real markets which can improve decision making efficiently.

Keywords

Total Asset Implicit Learning Agent Selection Learner Agent Negotiation Protocol 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wellman, M., Wurman, P.: Market-aware agents for a multi agent world. Robotics and Autonomous Systems 24, 115–125 (1998)CrossRefGoogle Scholar
  2. 2.
    Tsvetovat, M., Carley, K., Sycara, K.: Emergence of market segmentation: a multi-agent model, CASOS and Robotics Institute (2002)Google Scholar
  3. 3.
    Holland, J.H.: Properties of the bucket brigade. In: Proceedings of an International Conf. on Genetic Algorithms, Hillsdale, NJ (1985)Google Scholar
  4. 4.
    Schmidhuber, J.: Evolutionary principles in self-referential learning, or on learning how to learn the meta-meta, Institut fur Informatik, Technische Universitat Munchen (1987)Google Scholar
  5. 5.
    Irwin, D.E., Grit, L.E., Chase, J.S.: Balancing Risk and Reward in a Market-based Task Service, Report from Duke University (2004)Google Scholar
  6. 6.
    Garvey, T.D., Lowrance, J.D., Fischler, M.A.: An inference Technique for integrating knowledge from disparate sources. In: Proceedings of the 7th Int. Joint Conf. Artificial intelligence, Vancouver, B.C., pp. 319–325 (1981)Google Scholar
  7. 7.
    Chen, S., Rodriguez, O., Chee-Yoong, C., Shang, Y., Shi, H.: Personalizing Digital Libraries for Learners. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, Springer, Heidelberg (2001)Google Scholar
  8. 8.
    Hawryszkiewycz, I.T.: Agent Support for Personalized Learning Services. In: Proceedings of the 3rd IEEE Int’l Conf. Advanced Learning Technologies (ICALT 2003) (2003)Google Scholar
  9. 9.
    IMS Learner Information Package Specification, Downloaded: January 2006 (2006), http://www.imsglobal.org/profiles/
  10. 10.
    Ashoori, M., Nili, M., Moshiri, B.: Toward a model of an improved economy of agents. In: Proceedings of the 2nd Int’l Conf. on machine intelligence (November 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Maryam Ashoori
    • 1
  • Chun Yan Miao
    • 1
  • Angela Eck Soong Goh
    • 1
  • Wang Qiong
    • 2
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore
  2. 2.School of EducationPeking UniversityChina

Personalised recommendations