Modern Soldiers increasingly rely on computational and autonomous systems for the completion of their missions<sup>1,2</sup>. Challenges arise around the use of such systems that depend largely on the relationship between agents and human actors<sup>3</sup> . Improving agents in the field requires the development of adaptive, human-aware systems that learn behaviors based on the needs of their human counterparts, acting effectively as teammates rather than tools<sup>4</sup> . The development of such agent teammates is non-trivial, but recent advances in machine-learning and artificial-intelligence are promising. We identify deep reinforcement learning (RL)<sup>5</sup> , multi-agent RL<sup>6</sup> , and human-guided RL<sup>7</sup> as powerful tools for the creation of adaptive agent teammates. We propose a three-armed approach to the development of agent teammates that leverages these advances in RL. First, multi-agent deep learning can be used to solve increasingly complex problems. Second, human-guided reinforcement can be used to constrain agent behavior and speed up the discovery of optimal strategies. Third, human behavioral profiles derived from surveys of work-interest variables for specific military occupation specialty (MOS) codes can be used to tailor agent behavior to the needs of Soldiers. This approach addresses the necessary computational framework, the learning paradigm needed to discover behavior, and the human dimension that contextualizes behavior.
Visual analytics is a field of study which imparts knowledge through visual representations. The use of these visual representations provide a common method for analysts to sift through vast amounts of information and make informed decisions on critical matters. However, assisting the analyst in making connections with visual tools can be challenging if the information is not presented in an intuitive manner. This study aims to build upon our previous work and further investigate whether line thickness can be used as a valid visualization tool to improve situational awareness. In this paper, we follow-up on previous work to discuss research results exploring the impact that information complexity, measured as graph density, has on situational awareness. Our results indicate an increase in situational awareness, compared to non-enhanced visualizations for select graph densities. Furthermore, the results obtained in this study validate previous pilot study findings. The enhancement identified and validated with this research confirms that the line thickness visual cue represents a perceived information value tied to situational awareness. We conclude that this improved situational awareness and time savings occur from the decreased mental burden placed on the analyst.