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New Generation Computing

, Volume 10, Issue 2, pp 223–253 | Cite as

Enhanced qualitative physical reasoning system: Qupras

  • Masaru Ohki
  • Kiyokazu Sakane
  • Jun Sawamoto
  • Yuichi Fujii
Regular Papers
  • 23 Downloads

Abstract

There are many expert systems that use experimental knowledge for diagnostic analysis and design. However, there are two problems for systems using only experiential knowledge:
  1. (1)

    unexpected problems cannot be solved and

     
  2. (2)

    acquiring experiential knowledge from human experts is difficult.

     
To solve these problems, general principles or basic knowledge must be added to expert systems in addition to the experimental knowledge. In response, we previously proposed Qupras (Qualitative physical reasoning system) as a framework for basic knowledge. This system has two knowledge representations, one related to physical laws and the other to objects. By using this knowledge, Qupras reasons about the relations among physical objects, and predicts the next state of a physical phenomenon.
Recently, we have improved some of Qupras’ features, and this pater desctibes the following main enhancements:
  1. (1)

    inheritance for representation of objects,

     
  2. (2)

    new primitive representations to describe discontinuous change, and

     
  3. (3)

    control features for effective reasoning.

     

Keywords

Qualitative Reasoning Knowledge Representation Deep Knowledge Inheritance Discontinuous Change 

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References

  1. 1).
    Mizoguchi, R.,Foundation of Expert System, Expert System—Thery and Application. Nikkei-McGrawhill, pp. 15, 1987. [in Japanese]Google Scholar
  2. 2).
    Bobrow, D. G., “Special Volume on Qualitative Reasoning about Physical Systesm,”Artificial Intelligence, 24, 1984.Google Scholar
  3. 3).
    de Kleer, J. and Brown, J. S., “Qualitative Physics Based on Confluence,”Artificial Intelliegence, 24, pp. 7–83, 1984.CrossRefGoogle Scholar
  4. 4).
    Forbus, K. D., “Qualitative Process Theory,”Artificial Intelligence, 24, pp. 85–168. 1984.CrossRefGoogle Scholar
  5. 5).
    Kuipers, B., “Commonsense Reasoning about Causality: Deriving Behavior from Structure,”Artificial Intelligence, 24, pp. 169–203, 1984.CrossRefGoogle Scholar
  6. 6).
    Nishida, T. and Doshita, S., “Reasoning about Discontinuous Change,”AAAI-87, pp. 643–648, 1987.Google Scholar
  7. 7).
    Yamaguchi, T., Mizoguchi, R., Taoka, N., Kodaka, H., Nomura, Y., and Kakusho, O., “Basic Design of Knowledge Compiler Based on Deep Knowledge,”J. of Japanese Soc. for Artif. Intel., 2, pp. 333–340, 1987. [in Japanese]Google Scholar
  8. 8).
    Ohwada, H., Mizoguchi, F., and Kitazawa, Y., “A Method for Developing Diagnostic Systems based on Qualitative Simulation,”J. of Japanese Soc. for Artif. Intel., 3, pp. 617–626, 1988. [in Japanese]Google Scholar
  9. 9).
    Ohki, M. and Furukawa, K., “Toward Qualitative Resoning,”ICOT Technical Report, TR-221, 1986.Google Scholar
  10. 10).
    Ohki, M., Fujii, Y., and Furukawa, K., “Qualitative Reasoning based on Physical Laws,”Trans. Inf. Proc. Soc. Japan, 29, pp. 694–702, 1988. [in Japanese]Google Scholar
  11. 11).
    Ohki, M., Sawamoto, J., Sakane, K., and Fujii, Y., “A Constraint Logic Programming Language based on the Sup-Inf Method,”Proc. of 5th Conf. JSSST, pp. 49–52, 1988. [in Japanese]Google Scholar
  12. 12).
    Simmons, S., “Commonsense Arithmetic Reasoning,”AAAI-86, pp. 118–128, 1986.Google Scholar
  13. 13).
    Sakai, K. and Aiba, A., “CAL: A Theoretical Background of Constraint Logic Programming and Its Application,”ICOT Technical Report, TR-364, 1988.Google Scholar
  14. 14).
    Kuipers, B., “Qualitative Simulation of Mechanisms,”MIT LCS TM-274, 1985.Google Scholar
  15. 15).
    Swedish Institute of Computer Science,SICStus Prolog User’s Manual, 1989.Google Scholar

Copyright information

© Ohmsha, Ltd. and Springer 1992

Authors and Affiliations

  • Masaru Ohki
    • 1
  • Kiyokazu Sakane
    • 1
  • Jun Sawamoto
    • 1
  • Yuichi Fujii
    • 1
  1. 1.ICOT Research CenterInstitute for New Generation Computer TechnologyTokyoJapan

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