AI & SOCIETY

, Volume 8, Issue 1, pp 17–28

On qualitative modelling

Article
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Abstract

Fundamental assumptions behind qualitative modelling are critically considered and some inherent problems in that modelling approach are outlined. The problems outlined are due to the assumption that a sufficient set of symbols representing the fundamental features of the physical world exists. That assumption causes serious problems when modelling continuous systems. An alternative for intelligent system building for cases not suitable for qualitative modelling is proposed. The proposed alternative combines neural networks and quantitative modelling.

Keywords

Expert systems Modelling Qualitative modelling 

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References

  1. Ahonen, J.J. and Saarenmaa, H. (1991). Model-based reasoning about natural ecosystems: an algorithm to reduce the computational burden associated with simulating multiple biological agents. InProc 6th Symposium on Computer Science for Environmental Protection. pp. 193–200.Google Scholar
  2. Clacey, W.J. (1992). Model construction operators.Artificial Intelligence, 53. pp. 1–115.Google Scholar
  3. de Kleer, J. (1993). A view on qualitative physics.Artificial Intelligence, 59. pp. 105–114.Google Scholar
  4. Fishwick, P.A. (1992). An integrated approach to system modelling using a synthesis of artificial intelligence, software engineering and simulation methodologies.ACM Transactions on Modelling and Computer Simulation, 2. pp. 307–330.Google Scholar
  5. Futo, I. and Gregley, T. (1990).Artificial intelligence in simulation. Ellis Horwood, Chichester.Google Scholar
  6. Hayes, P.J. (1979). The naive physics manifesto. In Michie, D. (ed.).Expert Systems in the Micro-electronic Age. Edinburgh University Press, Edinburgh.Google Scholar
  7. Kuipers, B.J. (1993a). Reasoning with qualitative models.Artificial Intelligence, 59. pp. 125–132.Google Scholar
  8. Kuipers, B.J. (1993b). Qualitative simulation then and now.Artificial Intelligence, 59. pp. 133–140.Google Scholar
  9. Kuipers, B.J. (1985) The limits of qualitative simulation.In Proc of International Joint Conference on Artificial Intelligence. pp. 128–136.Google Scholar
  10. Lewis, T.G. and Smith, B.J. (1979).Computer Principles of Modelling and Simulation. Houghton Miffin, Boston.Google Scholar
  11. Miller, D.P., Firby, R.J., Fishwick, P.A., Franke, D.W. and Rothenberg, J. (1992). AI: What simulationists really need to know.ACM Transactions on Modelling and Computer Simulation, 2. pp. 269–284.Google Scholar
  12. Newell, A. and Simon, H. (1981). Computer science as empirical inquiry: symbols and search. Reprinted in: Haugeland, J. (ed.).Mind Design. MIT Press, Cambridge.Google Scholar
  13. Saarenmaa, H., Kaila, E., Nuutinen, T. and Kolstrom, T. (1991). Operational forest management planning with logic programming. In:Current Advances in the Use of Computers in Forest Research — Proc of the IUFRO Workshop February 11, 1991. Bulletins of the FFRI Vol 395, pp. 61–68.Google Scholar
  14. Sachs, E. (1987). Piecewise linear reasoning. In:Proc of AAAI-87. Seattle, Washington. pp. 655–659.Google Scholar
  15. Salmon, W.C. (1984).Scientific explanation and the causal structure of the world. Princeton University Press, Princeton.Google Scholar
  16. Sayre, K.M. (1965).Recognition: a study in the philosophy of artificial intelligence. University of Notre Dame Press, Notre Dame (Indiana).Google Scholar
  17. Thompson, J.R. (1989).Empirical model building. John Wiley & Sons, New York.Google Scholar
  18. Weld, D.S. and de Kleer, J. (eds.) (1990).Qualitative reasoning about physical systems. Morgan Kaufmann Publishers, San Mateo, California.Google Scholar
  19. Widman, L.E. and Loparo, K.A. (1989). Artificial intelligence, simulation and modelling: a critical survey. In: Widman, Loparo and Nielsen (op cit).Google Scholar
  20. Widman, L.E., Loparo, K.A. and Nielsen, N.R. (1989)Artificial Intelligence, simulation and modelling. John WIley & Sons, New York.Google Scholar
  21. Wittgenstein, L. (1971).Tractatus Logico-Philosophicus (Finnish translation). WSOY, Keuruu.Google Scholar
  22. Zeigler, B.P. (1976).Theory of Modelling and Simulation. Wiley, New York.Google Scholar

Copyright information

© Springer-Verlag London Limited 1994

Authors and Affiliations

  1. 1.Lappeenranta University of TechnologyLappeenrantaFinland

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