Skip to main content

An E-learning Support System Based on Qualitative Simulations for Assisting Consumers’ Decision Making

  • Conference paper
Innovations in Applied Artificial Intelligence (IEA/AIE 2004)

Abstract

In this paper, we propose an e-learning support system (LSDM) for assisting a buyers’ decision making by applying artificial intelligence technology. When buyers purchase an expensive item, they must carefully select it from many alternatives. The learning support system provides useful information that helps consumers to purchase goods. We employed qualitative simulations because the result of output of simulation is useful. It consists of a qualitative processing system and a quantitative calculation system. When buyers use the system, they first input goods information they want to purchase. The information input by buyers is used in the qualitative simulation. Next, they fill out a form concerned with the details of their budgets, the rate of loans, and several other factors. After that, the system integrates the results of simulation and the buyer’s input data and proposes plans to help their decision process. The system has several advantages: buyers can use it by simple input, they can understand process of simulation, and they can base their decision making on synthetic results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 74.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agell, N., Aguado, C.J.: A Hybrid Qualitative-Quantitative Classification Technique Applied to Aid Marketing Decisions. In: Proc. of 11th International Workshop on Qualitative Reasoning (2001)

    Google Scholar 

  2. Bredeweg, B., Forbus, K.: Qualitative Modeling in Education. AI magazine 24(4), 35–46 (2004); American Association for Artificial Intelligence

    Google Scholar 

  3. Chen, S.A., Wang, J., Yang, C.S.: Constructing Internet Futures Exchange for Teaching Derivatives Trading in Financial Markets. In: Proc. of International Conference on Computers in Education, vol. 2, pp. 1392–1395 (2002)

    Google Scholar 

  4. Forbus, K.D., Carney, K., Harris, R., Sherin, B.L.: A qualitative modeling environment for middle-school students: A progress report. In: International Workshop on Qualitative Reasoning (2001)

    Google Scholar 

  5. Forbus, K.D.: Helping Children Become Qualitative Modelers. Journal of the Japanese Society for Artificial Intelligence 17(4), 471–479 (2002)

    Google Scholar 

  6. Hata, S., Ohkawa, T., Komoda, N.: Backward Simulation Method in Qualitative Simulation. Transaction in IEE Japan 115-C(11), 1369–1376 (1995)

    Google Scholar 

  7. Kuipers, B.: Qualitative Reasoning. The MIT Press, Cambridge (1994)

    Google Scholar 

  8. Leelawong, K., Wang, Y., Biswas, G., Vye, N., Bransford, J., Schwartz, D.: Qualitative Reasoning Techniques to Support Learning by Teaching: The Teachable Agents Project. In: International Workshop on Qualitative Reasoning (2001)

    Google Scholar 

  9. Matsuo, T., Ito, T., Shintani, T.: A Structural Model for Qualitative Simulation-based a Contractors’ Decision Support System. In: Proc. of 2003 Tokai-Section Joint Conference of the Institutes of Electrical and Related Engineers, p. 251 (2003)

    Google Scholar 

  10. Matsuo, T., Ito, T., Hattori, H., Shintani, T.: A Qualitative Simulation Model for Buyers’ Decision Making. In: Proc. of the 20th Annual Conference of Japan Society for Software Science and Technology, JSSST (2003)

    Google Scholar 

  11. Nishida, T.: Qualitative Reasoning and its Application to Intelligent Problem Solving. IPSJ magasine 32(2), 105–117 (1991)

    Google Scholar 

  12. Russell, S., Norvig, P.: Artificial Intelligence –A Modern Approach- 2nd edn. Pearson Education International (1995)

    Google Scholar 

  13. Weir, R.S.G.: The Rigours of On-Line Student Assessment – Lessons from E-Commerce. In: Proc of International Conference on Computers in Education, vol. 2, pp. 840–843 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Matsuo, T., Ito, T., Shintani, T. (2004). An E-learning Support System Based on Qualitative Simulations for Assisting Consumers’ Decision Making. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_89

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-24677-0_89

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22007-7

  • Online ISBN: 978-3-540-24677-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics