  Current approaches to expert systems' reasoning under uncertainty fail to
capture the iterative revision process characteristic of intelligent human
reasoning. This paper reports on a system, called the Non-monotonic
Probabilist, or NMP (Cohen, et al., 1985). When its inferences result in
substantial conflict, NMP examines and revises the assumptions underlying the
inferences until conflict is reduced to acceptable levels. NMP has been
implemented in a demonstration computer-based system, described below, which
supports threat correlation and in-flight route replanning by Air Force pilots.
