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The author examines the real-time capture of experiential knowledge, based on formal modeling of human perception by presenting algorithms for the recognition and labeling of behavioral patterns. The nature of human perceptual models is discussed and the features of the FDS (formal description schema) model of human perception are presented. Real-time algorithms for recognition and labeling of behavioral patterns are then discussed. It is concluded that only by the approximation of modeling is it possible to gain efficacy in experiential knowledge representation and in `knowing'