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Handling uncertainty is an important component of most intelligent behaviour – so uncertainty resolution is a key step in the design of an artificially intelligent decision system (Clark, 1990). Like other aspects of intelligent systems design, the aspect of uncertainty resolution is also typically sought to be handled by emulating natural intelligence (Halpern, 2003; Ball and Christensen, 2009). A three-valued extension of classical (i.e. binary) fuzzy logic was proposed by Smarandache (2002) when he coined the term “neutrosophic logic” as a generalization of fuzzy logic to such situations where it is impossible to de-fuzzify the original fuzzy–valued variables via some tractable membership function into either of set T or its complement TC where both T and TC are considered crisp sets. In these cases one has to allow for the possibility of a third unresolved state intermediate between T and TC.

Recognition and Resolution of “Comprehension Uncertainty” in AI