Modeling a robotics operator manager in a tactical battlefield

Abstract

In a tactical battlefield, ground- and air- based robotic assets can be useful, but there is currently no one role to coordinate the use of multiple robots in the U.S. Army. The current work outlines and models the duties of a robotics operator manager (ROM), which could serve this role, using a theory of dynamic decision-making, instance-based learning (IBL). A model of the ROM is created that is based upon the IBL theory and that is intended to be a model of ROM's cognition. The IBL model first consults a Tactical Ground Reporting Network (TiGRNet) to recognize potential threat locations in a simulated battlefield. Then, the model decides whether a potential threat location warrants more investigation and if so weighs a number of factors to determine the type of robotic surveillance required (i.e., ground- or air- based). These factors include: the presence of certain attributes in a threat location, the utility of investigating a threat determining priority of investigation, the altitude of the terrain where the threat is located, whether there is time pressure, and the number of threat locations to investigate. The execution of the IBL model of the ROM generates predictions of the decisions made by a ROM under different factors. Future work in this area will focus on collecting human data to validate and adjust the predictions made by the IBL model of the ROM.