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.

翻译：在涉及高风险决策的关键系统中，完全自动化通常难以实现或并非理想选择。相反，人机协作团队能够取得更优效果。为研究、开发、评估和验证适用于此类协同的算法，需要能够支持人类与多个AI智能体交互的轻量级实验平台。然而，此类平台在国防环境中的应用案例较为有限。为填补这一空白，我们提出了Cogent人机协同实验平台，该平台实现了包含异构多智能体系统的人机协同（HMT）用例，可涉及学习型AI智能体、静态AI智能体及人类参与者。该平台基于Cogent框架构建，已被用于学术研究，包括今年在AAMAS会议的ALA研讨会上发表的研究成果[1]。我们期望借助该平台，推动关键系统与国防环境中人机协同领域的进一步研究。