A Human-Machine Team (HMT) is a group of agents consisting of at least one human and at least one machine all functioning collaboratively towards one or more common objectives. As industry and defense find more helpful, creative and difficult applications of AI-driven technology, the need to effectively and accurately model, simulate, test and evaluate HMTs will continue to grow and become even more essential. Going along with that growing need, new methods are required to evaluate whether a human-machine team is performing effectively as a team in testing and evaluation scenarios. You cannot predict team performance from knowledge of the individual team agents alone; interaction between the humans and machines – and interaction between team agents in general – increases the problem space and adds a measure of unpredictability. Collective team or group performance, in turn, depends heavily on how a team is structured and organized as well as the mechanisms, paths and substructures through which the agents in the team interact with one another – i.e., the team’s topology. With the tools and metrics for measuring team structure and interaction becoming more highly developed in recent years, we will propose and discuss a practical, topological HMT modeling framework that not only takes into account but is actually built around the team’s topological characteristics while still utilizing the individual human and machine performance measures. This presentation was prepared for the annual DATAWorks Conference, held April 26-28, 2022.
