FUZZY QUALITATIVE SIMULATION

The theory of fuzzy sets and the development of qualitative reasoning have had similar motivations: coping with complexity in reasoning abou t the properties of physical systems. An approach is described that ut ilizes fuzzy sets to develop a fuzzy qualitative simulation algorithm that allows a semiquantitative extension to qualitative simulation, pr oviding three significant advantages over existing techniques. Firstly , it allows a more detailed description of physical variables, through an arbitrary, but finite, discretisation of the quantity space. The a doption of fuzzy sets also allows common-sense knowledge to be represe nted in defining values through the use of graded membership, enabling the subjective element in system modelling to be incorporated and rea soned with in a formal way. Secondly, the fuzzy quantity space allows more detailed description of functional relationships in that both str ength and sign information can be represented by fuzzy relations holdi ng against two or multivariables. Thirdly, the quantity space allows o rdering information on rates of change to be used to compute temporal durations of the state and the possible transitions. Thus, an ordering of the evolution of the states and the associated temporal durations are obtained. This knowledge is used to develop an effective temporal filter that significantly reduces the number of spurious behaviors. Ex perimental results with the algorithm are presented and comparison wit h other recently proposed methods is made.