Abstract
Dynamic Processes are characterized by their evolutionary behaviour over time, defining a sequence of operation states of the System. Ascertaining the causes of a given system situation may become a difficult task, particularly in complex dynamic systems, since not all information required about the system state may be available at the precise moment.
Knowledge-Based Supervision is an outstanding Artificial Intelligence field contributing successfully to the progress of the control and supervion areas. Three essential factors characterize the function of a supervisor system: time constraints demanded from the supervision process, temporal updating of information coming from the dynamic system and generation of qualitative knowledge about the dynamic system.
In this work, an evolutionary data structure model conceived to generate, store and update qualitative information from raw data coming from a dynamic system is presented. This model is based on the concept of abstraction, in such a way an abstraction mechanism to generate qualitative knowledge about the dynamic system which the Knowledge-Based Supervisor is based on, is triggered according to some pre established considerations, among which real time constraints play a special role.
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Fiol-Roig, G. (2002). Knowledge Representation Model for Dynamic Processes. In: Escrig, M.T., Toledo, F., Golobardes, E. (eds) Topics in Artificial Intelligence. CCIA 2002. Lecture Notes in Computer Science(), vol 2504. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36079-4_4
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DOI: https://doi.org/10.1007/3-540-36079-4_4
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