  The last few years has seen a growing debate about techniques for managing
uncertainty in AI systems. Unfortunately this debate has been cast as a rivalry
between AI methods and classical probability based ones. Three arguments for
extending the probability framework of uncertainty are presented, none of which
imply a challenge to classical methods. These are (1) explicit representation
of several types of uncertainty, specifically possibility and plausibility, as
well as probability, (2) the use of weak methods for uncertainty management in
problems which are poorly defined, and (3) symbolic representation of different
uncertainty calculi and methods for choosing between them.
