面向未来指挥与控制的规模化交互式机器学习
Scalable Interactive Machine Learning for Future Command and Control
AI总结：
本文针对未来指挥与控制中决策时效性和复杂性问题，提出规模化交互式机器学习（SIML）框架，从人机交互算法、韧性团队构建和规模扩展三个方向推动人机协同决策。
摘要：
未来战争将要求指挥与控制（C2）人员在复杂且可能定义不清的态势中，以不断缩短的时间尺度做出决策。鉴于对稳健决策过程和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变革C2作战流程，以确保在快速变化的作战环境中具备适应性和效率。我们提议利用交互式机器学习领域近期取得的突破性进展，在该领域中，人类可以与机器学习算法协同合作，以引导机器学习算法的行为。本文指出了当前最先进科学和技术中的若干差距，未来的工作应解决这些差距，以将这些方法扩展到复杂C2环境中发挥作用。具体而言，我们描述了三个研究重点领域，它们共同旨在实现规模化交互式机器学习（SIML）：1）开发人机交互算法，以支持在复杂、动态态势中进行规划；2）通过优化角色、配置和信任来培养具有韧性的人机团队；3）扩展算法和人机团队的规模，以在一系列潜在环境和态势中实现灵活性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
