Uncertainty in computational perception and cognition

Humans often mimic nature in the development of new machines or systems. The human brain, particularly its faculty for perception and cognition, is the most intriguing model for developing intelligent systems. Human cognitive processes have a great tolerance for imprecision or uncertainty. This is of great value in solving many engineering problems as there are innumerable uncertainties in real-world phenomena. These uncertainties can be broadly classified as either uncertainties arising from the random behavior of physical processes or uncertainties arising from human perception and cognition processes. Statistical theory can be used to model the former, but lacks the sophistication to process the latter. The theory of fuzzy logic has proven to be very effective in processing the latter. The methodology of computing with words and the computational theory of perceptions are branches of fuzzy logic that deal with the manipulation of words that act as labels for perceptions expressed in natural language propositions. New computing methods based on fuzzy logic can lead to greater adaptability, tractability, robustness, a lower cost solution, and better rapport with reality in the development of intelligent systems.