Possibilistic logic - An overview

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Handbook of The History of Logic, 2014, 1st, 9 pp. 283 - 342
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Uncertainty often pervades information and knowledge. For this reason, the handling of uncertainty in inference systems has been an issue for a long time in artificial intelligence (AI). Indeed rather early in the history of AI, in the early 1970's, the second expert system to be designed, MYCIN [Buchanan and Shortliffe, 1984], was the occasion of proposing an original setting for the representation of uncertainty in terms of degree of belief, degree of disbelief, and certainty factor, with empirical rules for combining them [Shortliffe, 1976]. Since that time, different new proposals have been developed for representing uncertainty including imprecise probabilities [Walley, 1991], belief function-based evidence theory [Shafer, 1976; Yager and Liu, 2008], possibility theory [Zadeh, 1978; Dubois and Prade, 1988], while Bayesian probabilities [Pearl, 1988; Jensen, 2001] have become prominent at the forefront of AI methods, challenging the original supremacy of logical representation settings [Minker, 2000].
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