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

An Application of Non-Monotonic Probabilistic Reasoning to Air Force Threat Correlation