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

TL;DR

This paper introduces the Non-monotonic Probabilist (NMP), a system that revises uncertain inferences iteratively to better mimic human reasoning, demonstrated in threat correlation and route replanning for Air Force pilots.

Contribution

It presents a novel non-monotonic probabilistic reasoning system that revises assumptions to resolve conflicts, improving expert system reasoning under uncertainty.

Findings

NMP effectively reduces inference conflicts.

System supports threat correlation and route replanning.

Demonstrated in Air Force operational scenarios.

Abstract

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.