Self-Explanatory Automation for Fighter Aircraft

Driven by advances in artificial intelligence, machine learning, and cognitive modelling, Human-Autonomy Teaming (HAT) has become increasingly practical and applicable. As such, it has been argued that we are entering a new interaction paradigm. In aviation, we see this paradigm shift on the horizon as unmanned aircraft are supposed to act as human-like wingmen in the near future. In such Manned-Unmanned Teaming (MUM-T), it is assumed that intent-aware systems that can account for and adapt to their human partner’s agency enable more effective and fluent teamwork.

The project studies self-explanatory automation for fighter aircraft through algorithms and design principles for more transparent interaction with highly automated systems. This will be achieved through the identification, modelling, and simulation of critical situations (for instance, shifts in levels of automation), for which automated support will be developed and empirically evaluated. Currently, the project addresses the first stage of designing intent-aware systems, by developing a human-centric modelling approach to intent for HAT in aviation. The project uses a scenario-based approach, where critical situations are analyzed to understand and model intent content. Results are expected to benefit human-automation cooperation and human understanding of the system, particularly supporting the learning process during training.

The project is funded by VINNOVA.