Active Measurement and Human Augmentation
Modern sociotechnical systems are exemplified by individuals, teams and organizations working in concert with advanced and autonomous technologies to accomplish complex tasks. The challenge is to design these systems to capitalize on both human and machine capabilities by letting humans do what they do best and letting machines do what they do best to increase effectiveness and efficiency. In other words, the solution is to design systems with the human-in-the-loop; machines that are designed to adapt to or augment rather than “replace” the humans in the system (e.g., Hancock, Chignell, & Lowenthal, 1985; Parasuraman et al., 1992; Rouse, 1988). The key lies in “big data” generated not by machines but by humans. Warfighters, whether in training or operations produce massive amounts of information through neural, physiological, and behavioral signals that can be decoded to inform automated technologies to more effectively augment performance. These data can feed a Sense-Assess-Augment paradigm that can bridge the gap from data to effective human augmentation.
This presentation will focus on the development of sensor suites and analytic approaches to develop a “Quantified Warrior;” an enhanced methodology for representing information about the human operator to cross-service DoD autonomous systems and efficient test and evaluation processes for validating the effective integration of humans with autonomous systems. In our work, our objective is to develop and apply such a methodology for application toward autonomous systems agnostic to any specific work domain. We are conducting laboratory-based research at the Air Force Research Laboratory to determine a common minimal set of measures, analysis techniques, and transfer protocols that will communicate the cognitive state of the operator to autonomous systems in real time mission settings. The result of this work will be the tools, methods, and standardized interface protocols that will be a key enabling technology for the development of flexible, user-conscious autonomy. The anticipated end state is to provide the technological basis for future advancements in flexible, human-system collaborations.