  Full automation is often not achievable or desirable in critical systems with
high-stakes decisions. Instead, human-AI teams can achieve better results. To
research, develop, evaluate, and validate algorithms suited for such teaming,
lightweight experimentation platforms that enable interactions between humans
and multiple AI agents are necessary. However, there are limited examples of
such platforms for defense environments. To address this gap, we present the
Cogment human-machine teaming experimentation platform, which implements
human-machine teaming (HMT) use cases that features heterogeneous multi-agent
systems and can involve learning AI agents, static AI agents, and humans. It is
built on the Cogment platform and has been used for academic research,
including work presented at the ALA workshop at AAMAS this year [1]. With this
platform, we hope to facilitate further research on human-machine teaming in
critical systems and defense environments.
