certainty factors

Certainty factors are way of performing uncertain reasoning. While Bayesian reasinong demands that belief be precisely coded as a single probability-like number, confidence factors allow a range with belief for and against a particlar conclusion to build as new evidence is found.

Initially , each initial fact/evidence is initially given a certainty factor where positive in the range +1 to -1, where positive values denote evidence for and negative values denote evidence against, with zero meaning no evidence and the extremes +1/-1 meaning incontrovertable evidence for/against). Derived facts are given two values, a measure of belief and a measure of diselief (each [0,1]) based on evidence, and these are accumulated as further positive or negative evidence arises. This works well in medical domains where there can be evidence both for and against a diagnosis. Certainty factors were used in the successful early expert system {{MYCIN} and still used in expert systems including neural network variants. However, they are less popular now than Bayesian methods and fuzzy reasoning.