Understanding situational awareness in multi-unit supervisory control through data-mining and modeling with real-time strategy games

As robots become increasingly capable and autonomous, the role of a human operator may be to supervise multiple robots and intervene to handle problems and provide strategic guidance. In such cases, the extent to which HRI tools support the human supervisor´s situational awareness (SA) and ability to intervene in an appropriate and timely fashion will constrain the scale of operations (e.g., the number of robots; the complexity of tasks) that can reasonably be supervised by a single person. One approach to understanding how humans might acquire, maintain, and use situational awareness in multi-robot supervision tasks is to look at video games that require similar activities. We describe our initial efforts at analyzing and modeling data from Real-Time Strategy (RTS) games with the goal of answering basic questions about the nature of situational awareness and supervisory control of multiple semi-autonomous agents.