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With the exponential growth of technology, future military operations will be comprised of not just ground operations but a multi-domain battlespace. Paramount to mission success will be the reliance on intelligent adaptive computational agents and effective human-agent teaming. An agent teammate can assist the Soldier with tasks that may be seen as physically difficult, cognitively fatiguing, or high risk. However, successful teaming is compromised when an agent lacks the attributes that contribute to effective human-human collaboration, such as knowledge about team-members’ work preferences or capabilities. One way to provide agents with a sense of team-member preferences or capabilities is to quantitatively characterize such preferences as a function of the job the human intends to perform. To address this, we analyzed a modified survey from the Army Research Institute that is commonly used to identify work-abilities variables in military personnel based on the service member’s Military Occupational Specialty (MOS). Using machine learning techniques, statistical comparisons are made in order to quantitatively assess populationaveraged responses that Soldiers from various MOS codes provided on an Army Abilities questionnaire. Similarities and differences across groupings of MOS codes can provide a set of observations that might be parametrized into a computational agent’s framework. The goal of this work is to identify MOS code related parameters that might be incorporated into a computational agent’s framework in the future development of flexibly adaptive agents for Soldieragent teams.
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