M-OAT Shared Meta-Model Framework for Effective Collaborative Human-Autonomy Teaming

Integrating humans and autonomous machines in teams for the successful completion of complex, multi-objective tasks in dynamic or unknown environments can help improve the safety and efficiency of team members. For effective cooperation, human-machine teams require understanding team members' unique potentials, interdependent decision-making, and trust among all team members. To develop a framework that supports the human-machine teaming required of complex, multi-objective tasks, shared mental models, cognitive representations, and multi-directional trust calibration need to be investigated. Providing a shared mental model, cognitive load understanding, and situational awareness to agents allows human-machine teams to adapt to shortcomings and unexpected environmental threats, independently or conjointly make time-sensitive decisions, and improve safety of team members and efficiency of task performance. The goal of our research is to create a multi-objective decision-support framework that encourages the incorporation of trusted autonomous systems for effective cooperation in ad-hoc heterogeneous collaborative teams.