Future warfare will require Command and Control (C2) personnel to make decisions at shrinking timescales in complex and potentially ill-defined situations. Given the need for robust decision-making processes and decision-support tools, integration of artificial and human intelligence holds the potential to revolutionize the C2 operations process to ensure adaptability and efficiency in rapidly changing operational environments. We propose to leverage recent promising breakthroughs in interactive machine learning, in which humans can cooperate with machine learning algorithms to guide machine learning algorithm behavior. This paper identifies several gaps in state-of-the-art science and technology that future work should address to extend these approaches to function in complex C2 contexts. In particular, we describe three research focus areas that together, aim to enable scalable interactive machine learning (SIML): 1) developing human-AI interaction algorithms to enable planning in complex, dynamic situations; 2) fostering resilient human-AI teams through optimizing roles, configurations, and trust; and 3) scaling algorithms and human-AI teams for flexibility across a range of potential contexts and situations.

翻译：未来战争要求指挥与控制（C2）人员在不断缩短的时间尺度内，在复杂且可能定义不清的情境中做出决策。鉴于对稳健决策过程与决策支持工具的需求，人工智能与人类智能的融合有望彻底革新C2运作流程，以确保在快速变化的作战环境中保持适应性与效率。我们提议利用近期交互式机器学习领域突破性的进展——即人类可与机器学习算法协作以引导其行为——来解决上述挑战。本文识别了当前科学技术中若干亟待解决的空白，未来工作需填补这些空白才能将这些方法扩展应用于复杂的C2场景。具体而言，我们描述了三个研究重点领域，旨在共同实现可扩展交互式机器学习（SIML）：1）开发人机交互算法，以支持复杂动态情境中的规划；2）通过优化角色、配置与信任机制，培育韧性人机协作团队；3）扩展算法与人机协作团队的规模，使其能够灵活适应多种潜在情境与场景。