An approach is described that utilizes fuzzy sets to develop a fuzzy qualitative simulation algorithm that allows a semiquantitative extension to qualitative simulation, providing 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 adoption of fuzzy sets also allows common-sense knowledge to be represented in defining values through the use of graded membership, enabling the subjective element in system modelling to be incorporated and reasoned with in a formal way. Secondly, the fuzzy quantity space allows more detailed description of functional relationships in that both strength and sign information can be represented by fuzzy relations holding against two or multivariables. Thirdly, the quantity space allows ordering 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.>

A formal classification of primitive tasks for knowledge-based system applications in industrial automation is presented. The classification is based on a system theoretic perspective using the direction of temporal reasoning as the metric for classification. This classification is canonical with respect to time and can, therefore, be used to define primitive elements in constructing more complex tasks that are termed systems. Tasks and systems are used to define a multilayered architecture that provides both a problem decomposition, relating systems to tasks, and a set of generic knowledge-based tools, each satisfying a task description and determined by an epistemological analysis of the domain. A comparison between the architecture presented and other approaches to 'knowledge-level' analysis is given.>