A synthetic view of approximate reasoning techniques

• Authors: Henri Prade
• Venue: IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
• Year: 1983

This paper presents a review of different approximate reasoning techniques which have been proposed for dealing with uncertain or imprecise knowledge, especially in expert systems based on production rule methodology. Theoretical approaches such as Bayesian inference, Shafer's belief theory or Zadeh's possibility theory as well as more empirical proposals such as the ones used in MYCIN or in PROSPECTOR, are considered. The presentation is focused on two basic inference schemes : the deductive inference and the combination of several uncertain or imprecise evidences relative to a same matter. Several kinds of uncertainty are taken into account in the models which are described in the paper : different degrees of certainty or of truth may be associated with the observed or produced facts or with the " if.., then..." rules; moreover the statements of facts or of rules may be imprecise or fuzzy and the values of the degrees of certainty which are used may be only approximately known. An extensive bibliography, to which it is referred in the text, is appended.