• Bruce D’Ambrosio
Part of the Symbolic Computation book series (SYMBOLIC)


In this book we explore the use of linguistic variables as a semiquantitative extension to the qualitative value and relationship representations in qualitative process theory, for application in fuzzy logic control. Qualitative process (QP) theory, developed by Kenneth Forbus, describes the form of qualitative theories about the dynamics of physical systems. Its central thesis is that all change in such systems is the result of active processes, and that these processes should be explicitly represented and reasoned about. Much of QP theory’s power derives from the qualitative representations used for the values of individual continuous-state parameters and the relations between these parameters. Qualitative descriptions are important because they provide the ability to reason with incomplete information and can guide the application of more detailed quantitative theories when additional information is available. Forbus has demonstrated that QP theory can be used to derive many significant deductions given only weak qualitative descriptions of variable values and relationships. For example, QP theory can be used to determine that the water in Fig. 1.1 will heat up and eventually boil, and that the container may eventually explode. However, there are at least three limitations to the current ability to analyze this situation using QP theory:
  1. 1.

    QP theory cannot be used to estimate how likely it is that there will be an explosion.

  2. 2.

    QP theory is unable to analyze situations only slightly more complicated than the one shown. For example, if we include a model for heat loss from the container to the surrounding environment, QP theory can no longer predict whether or not the water will boil.

  3. 3.

    QP theory provides little basis for reasoning about continuous control actions; for example, how much or when should the heat be turned down to avoid explosion?



Linguistic Variable Qualitative Description Qualitative Representation Continuous Control Fuzzy Logic Control 
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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Copyright information

© Springer-Verlag New York Inc. 1989

Authors and Affiliations

  • Bruce D’Ambrosio
    • 1
  1. 1.Department of Computer ScienceOregon State UniversityCorvallisUSA

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