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Naïve Physics

  • François E. Cellier

Abstract

Until this point, we have focused on a single question throughout this entire text: How can we get computer programs to mimic the behavior of physical systems. In this chapter, as well as in the following chapters, we shall deal with quite a different issue: We shall try to understand how humans model the behavior of physical systems in the absence of a computer, i.e., how they reason about the functioning of a device or process. In other words, we shall try to model the process of understanding itself. Naïve physics is one methodology that can address this question. Other methodologies will be discussed in due course.

Keywords

Consistency Constraint Continuity Constraint Influence Diagram Negative Real Axis Qualitative Simulation 
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.

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References

  1. [12.1]
    Earl Babbie (1989), The Practice of Social Research, fifth edition, Wadsworth Publishing Company, Belmont, Calif.Google Scholar
  2. [12.2]
    Daniel G. Bobrow, Ed. (1985), Qualitative Reasoning about Physical Systems, MIT Press, Cambridge, Mass.Google Scholar
  3. [12.3]
    François E. Cellier (1986), “Enhanced Run—Time Experiments for Continuous System Simulation Languages,” Proceedings SCS MultiConference on Languages for Continuous System Simulation (F.E. Cellier, ed.), SCS Publishing, San Diego, Calif., pp. 78–83.Google Scholar
  4. [12.4]
    François E. Cellier and C. Magnus Rimvall (1989), “Matrix Environments for Continuous System Modeling and Simulation,” Simulation, 52 (4), pp. 141–149.CrossRefGoogle Scholar
  5. [12.5]
    François E. Cellier and Nicolas Roddier (1991), “Qualitative State Spaces: A Formalization of the Naive Physics Approach to Knowledge—Based Reasoning,” Proceedings AI, Simulation and Planning in High Autonomy Systems (P.A. Fishwick, B.P. Zeigler, and J.W. Rozenblit, eds.), IEEE Computer Society Press, Los Alamitos, Calif.Google Scholar
  6. [12.6]
    R. G. Coyle (1977), Management System Dynamics, John Wiley and Sons, London, U.K.Google Scholar
  7. [12.7]
    Ernest Davis (1987), “Constraint Propagation with Interval Labels,” Artificial Intelligence, 32, pp. 281–331.MathSciNetCrossRefzbMATHGoogle Scholar
  8. [12.8]
    Johan de Kleer and John S. Brown (1984), “A Qualitative Physics Based on Confluences,” Artificial Intelligence, 24, pp. 7–83.CrossRefGoogle Scholar
  9. [12.9]
    Richard C. Dorf (1989), Modern Control Systems, fifth edition, Addison—Wesley, Reading, Mass.zbMATHGoogle Scholar
  10. [12.10]
    Kenneth D. Forbus (1984), “Qualitative Process Theory,” Artificial Intelligence, 24, pp. 85–168.CrossRefGoogle Scholar
  11. [12.11]
    Dedre Gentner and Albert Stevens (1983), Mental Models, Lawrence Erlbaum Associates, Hillsdale, N.J.Google Scholar
  12. [12.12]
    Benjamin Kuipers (1986), “Qualitative Simulation,” Artificial Intelligence, 29, pp. 289–338.MathSciNetCrossRefzbMATHGoogle Scholar
  13. [12.13]
    Benjamin Kuipers and Adam Farquhar (1987), QSIM: A Tool for Qualitative Simulation, Internal Report: Artificial Intelligence Laboratory, The University of Texas, Austin.Google Scholar
  14. [12.14]
    Antony J. Morgan (1988), The Qualitative Behaviour of Dynamic Physical Systems, Ph.D. dissertation, Wolfson College, The University of Cambridge, Cambridge, U.K.Google Scholar
  15. [12.15]
    Antony J. Morgan (1990), “Accuracy in Qualitative Descriptions of Behaviour,” Proceedings Winter Simulation Conference, New Orleans, La., pp. 520–526.Google Scholar
  16. [12.16]
    Brian C. Williams (1984), “Qualitative Analysis of MOS Circuits,” Artificial Intelligence, 24, pp. 281–346.CrossRefGoogle Scholar

Bibliography

  1. [B12.1]
    Paul A. Fishwick (1988), “The Role of Process Abstraction in Simulation,” IEEE Trans. Systems, Man and Cybernetics, 18 (1), pp. 18–39.CrossRefGoogle Scholar
  2. [B12.2]
    Paul A. Fishwick and Paul A. Luker, Eds. (1991), Qualitative Simulation, Modeling, and Analysis, Springer-Verlag, New York.Google Scholar
  3. [B12.3]
    Paul A. Fishwick and Bernard P. Zeigler (1990), “Qualitative Physics: Towards the Automation of Systems Problem Solving,” Proceedings AI, Simulation and Planning in High Autonomy Systems (B.P. Zeigler and J.W. Rozenblit, eds.), IEEE Computer Society Press, Los Alamitos, Calif., pp. 118–134.Google Scholar
  4. [B12.4]
    Patrick J. Hayes (1979), “The Naive Physics Manifesto,” in: Expert Systems in the Micro-Electronic Age (D. Michie, ed.), Edinburgh University Press, Edinburgh, Scotland, pp. 242–270.Google Scholar
  5. [B12.5]
    Raman Rajagopalan (1986), “Qualitative Modelling and Simulation: A Survey,” Proceedings AI Applied to Simulation (E. Kerckhoffs, G.C. Vansteenkiste, and B.P. Zeigler, eds.), Simulation Series, 18(1), Springer-Verlag, Berlin, pp. 9–26.Google Scholar
  6. [B12.8]
    Lawrence E. Widman, Kenneth A. Loparo, and Norman R. Nielsen, Eds. (1989), Artificial Intelligence, Simulation and Modeling, John Wiley and Sons, New York.Google Scholar
  7. [B12.7]
    Bernard P. Zeigler and Jerzy W. Rozenblit, Eds. (1990), AI, Simulation and Planning in High Autonomy Systems, IEEE Computer Society Press, Los Alamitos, Calif.Google Scholar

Copyright information

© Springer Science+Business Media New York 1991

Authors and Affiliations

  • François E. Cellier
    • 1
  1. 1.Department of Electrical and Computer Engineering and Applied Mathematics ProgramUniversity of ArizonaTucsonUSA

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