Synthetic Agents for the Federal Aviation Administration’s (FAA’s) Next Generation Air Traffic Control Systems (NextGen)
In anticipation of the Federal Aviation Administration’s Next Generation Air Traffic Control System (NextGen), advanced modeling and simulation methods are needed to provide enhanced training and simulation. One of the challenges expressed by the FAA is that in preparation for transition to NextGen, changes in existing controller roles, responsibilities and tasks need to be explored in a simulated fashion in order to empirically assess the optimal mix of roles and responsibilities. Intelligent agents represent a promising technology for measuring the necessary exploration of multiple combinations of alternative task/role/responsibility configurations. CHI Systems is using its Advanced Speech Interactive Synthetic Teammate Authoring Toolkit® (ASIST‐AT®) to develop synthetic pilot and air traffic controller models based on a representative sample of ATC tasks, with sufficient richness to interact behaviorally and verbally with human controllers and pilots in simplified Human‐in‐the‐loop (HITL) experiments.
Joint Systems Engineering Methodology (JSEM)
CHI Systems developed a systems development (SD) methodology that draws on the strengths of existing SD approaches in addition to the offerings of cognitive systems engineering (CSE). The methodology is called the Joint Systems Engineering Methodology, (JSEM) a name that refers to the dual and complementary objectives of designing systems that treat humans and machines as co-dependent and joint contributors to cognitive work and of achieving collaboration among the various disciplines that participate in SD.
This effort derived from the need for systems development frameworks and methods that better accommodate the design of systems to be used in increasingly complex and information-rich work environments—i.e., complex, cognitive systems. The work additionally was based on the premise that the CSE discipline, with its emphasis on designing for complex cognitive work environments, may be a rich and valuable source of principles and methods for providing SD with the improvements it needs as it faces increasingly complicated development environments and objectives.
The development of the JSEM framework and methodology for SD was based on the results of activities that include the assessment of past SD cases and critical analyses of SD; case-based interviews with SD and systems engineering professionals to identify obstacles and opportunities for improving current SD practices; and the review and assessment of CSE methods that are relevant to identified obstacles and opportunities for improvements.