The theory of fuzzy sets and the development of qualitative reasoning have had similar motivations: coping with complexity in reasoning about the properties of physical systems. This talk will review an approach that utilises fuzzy sets to develop a fuzzy qualitative simulation algorithm.
The resultant algorithm provides several significant advantages over conventional qualitative simulation techniques, allowing a) subjective elements in system modelling to be incorporated and reasoned with in a formal way, b) functional dependencies between system variables to be described semi-quantitatively rather than merely monotonically, and c) the evolution of system states to be ordered and the associated temporal durations computed. This advanced qualitative simulation technique offers the prospect of providing a basis for automated solutions to a wide range of application problems. In particular, it has been employed to form the heart of model-based reasoning for performing tasks such as fault diagnosis, systems design and control, and industrial training. This talk will give an outline of some of such work, showing how the fuzzy qualitative simulation algorithm helps achieving these goals.