The Army plans to integrate artificial intelligence (AI)/machine learning (ML) and other intelligent decision-making aids into future dismounted Warfighter systems to augment situational awareness and target acquisition capabilities. However, due to the unique constraints of dismounted operations, successful implementation of intelligent decisionmaking aids in dismounted systems necessitates a human-in-the-loop approach, which includes the ability for the Warfighter to provide feedback to the autonomous system. Human-in-the-loop feedback can augment current machine learning techniques by reducing the size of datasets needed to train algorithms and allow algorithms to be flexible and adaptive to changing battlespace conditions. As such, research is required to define the bidirectional interactions between man and machine in this context, to optimize human-intelligent agent teaming for the dismounted Warfighter. In this paper, we focus on a specific application of dismounted Human-AI interaction to weapon mounted target acquisition (small arms fire control systems) and discuss issues pertaining to an important component of this optimization: how intelligent information is communicated to the end user. We consider how the intelligent information is presented to the Warfighter, and what underlying cognitive and perceptual processes can be leveraged to optimize teamed decision making. Such factors are critical to the successful implementation of human-in-the-loop AI in dismounted applications and ultimately the effectiveness of intelligent decision-making aids.
Target recognition and identification is a problem of great military and scientific importance. To examine the correlation between target recognition and optical magnification, ten U.S. Army soldiers were tasked with identifying letters on targets at 800 and 1300 meters away. Letters were used since they are a standard method for measuring visual acuity. The letters were approximately 90 cm high, which is the size of a well-known rifle. Four direct view optics with angular magnifications of 1.5x, 4x, 6x, and 9x were used. The goal of this approach was to measure actual probabilities for correct target identification. Previous scientific literature suggests that target recognition can be modeled as a linear response problem in angular frequency space using the established values for the contrast sensitivity function for a healthy human eye and the experimentally measured modulation transfer function of the optic. At the 9x magnification, the soldiers could identify the letters with almost no errors (i.e., 97% probability of correct identification). At lower magnification, errors in letter identification were more frequent. The identification errors were not random but occurred most frequently with a few pairs of letters (e.g., O and Q), which is consistent with the literature for letter recognition. In addition, in the small subject sample of ten soldiers, there was considerable variation in the observer recognition capability at 1.5x and a range of 800 meters. This can be directly attributed to the variation in the observer visual acuity.