An Application of Non-Monotonic Probabilistic Reasoning to Air Force Threat Correlation
This addresses the need for more adaptive reasoning in expert systems for Air Force pilots, though it appears incremental as it builds on existing probabilistic reasoning concepts.
The paper tackles the problem of expert systems' inability to mimic human iterative reasoning under uncertainty by introducing the Non-monotonic Probabilist (NMP) system, which revises assumptions to reduce conflicts, and demonstrates its application in an Air Force threat correlation and route replanning system.
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.