Skip to main content
SpringerLink
Log in

Problems in Representing Liquid Tankswith Monotonicity Constraints: A Case Study inModel-Imposed Limitations on the Coverage ofQualitative Simulators

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

We subject a basic qualitative modelwhich appears throughout the qualitativereasoning literature (the ``bathtub'' or liquidtank model) to a detailed theoretical analysisof its representation properties. We show thatthe standard model for this family of systemsdoes not cover the intuitive concept ofreal-world tanks, in that there are both simpletanks that do not obey the model, and thatthere are physically impossible shapes that doobey it and get considered by qualitativereasoners using the model. We demonstrate thatthese modeling problems may lead to a markeddecrease in the usefulness of employingqualitative reasoners in some cases. Weconclude that one should be careful aboutmaking both the assumptions required by themodel and the algorithm, and the family ofsystems that are actually reasoned about,explicit in the presentation of qualitativereasoners' output.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Catino, C. A. & Ungar, L. H. (1995). A Model-Based Approach to Automated Hazard Identification of Chemical Plants. Computers and Chemical Engineering 41: 97–109.

    Google Scholar 

  • Clancy, D. J. (1997). Solving Complexity and Ambiguity Problems within Qualitative Simulation. Ph.D. diss., Department of Computer Sciences, The University of Texas at Austin.

    Google Scholar 

  • Dalle Molle, D. T., Kuipers, B. J. & Edgar, T. F. (1988). Qualitative Modeling and Simulation of Dynamic Systems. Computers and Chemical Engineering 12: 835–866.

    Google Scholar 

  • de Kleer, J. & Brown, J. S. (1984). A Qualitative Physics Based on Confluences. Artificial Intelligence 24: 7–83.

    Google Scholar 

  • Forbus, K. D. (1984). Qualitative Process Theory. Artificial Intelligence 24: 85–168.

    Google Scholar 

  • Gazi, E., Seider, W. D. & Ungar, L. H. (1997). A Non-Parametric Monte Carlo Technique for Controller Verification. Automatica 33: 901–906.

    Google Scholar 

  • Halliday, D. & Resnick, R. (1978), Physics. John Wiley & Sons.

  • Ironi, L., Stefanelli, M. & Lanzola, G. (1990). Qualitative Models in Medical Diagnosis. Artificial Intelligence in Medicine 2: 85–101.

    Google Scholar 

  • Kaul, N., Biswas, G. & Bhuva, B. (1994). An AI Approach to Multi-Level, Mixedmode Qualitative Simulation of CMOS ICs. Computers and Electrical Engineering, An International Journal 20: 369–382.

    Google Scholar 

  • Kuipers, B. (1994). Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge. Cambridge, MA: MIT Press.

    Google Scholar 

  • Kuipers B. & Åström, K. (1994). The Composition and Validation of Heterogeneous Control Laws. Automatica 30: 233–249.

    Google Scholar 

  • Kuipers, B., Chiu, C., Dalle Molle, D. T. & Throop, D. (1991). Higher-Order Derivative Constraints in Qualitative Simulation. Artificial Intelligence 51: 343–379.

    Google Scholar 

  • Lang, K. R., Moore, J. C. & Whinston, A. B. (1995). Computational Systems for Qualitative Economics. Computational Economics 8: 1–26.

    Google Scholar 

  • Rowe, N. C. (1997). Obtaining Optimal Mobile-Robot Paths with Nonsmooth Anisotropic Cost Functions Using Qualitative-state Reasoning. Int. J. of Robotics Research 16: 375–399.

    Google Scholar 

  • Say, A. C. C. & Kuru, S. (1996). Qualitative System Identification: Deriving Structure from Behavior. Artificial Intelligence 83: 75–141.

    Google Scholar 

  • Shults, B. & Kuipers, B. (1997). Proving Properties of Continuous Systems: Qualitative Simulation and Temporal Logic. Artificial Intelligence 92: 91–129.

    Google Scholar 

  • Trelease, R. B. & Park., J. (1996). Qualitative Process Modeling of Cell-Cell-Pathogen Interactions in the Immune System. Computer Methods and Programs in Biomedicine 51: 171–181.

    Google Scholar 

  • Weld, D. S. & de Kleer, J. (eds.) (1990). Readings in Qualitative Reasoning About Physical Systems. San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  • Williams, B. C. (1988). MINIMA: A Symbolic Approach to Qualitative Algebraic Reasoning. In Proceedings of the 7th National Conf. on Artificial Intelligence, 264–269. San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

Download references

Authors
  1. A.C. Cem Say
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Say, A.C. Problems in Representing Liquid Tankswith Monotonicity Constraints: A Case Study inModel-Imposed Limitations on the Coverage ofQualitative Simulators. Artificial Intelligence Review 17, 291–317 (2002). https://doi.org/10.1023/A:1015510017160

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1015510017160

Search

Navigation