Scalable Interactive Machine Learning for Future Command and Control - Research Focus Areas

Aug 2, 2026

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Future warfare demands Command and Control (C2) personnel to decide at shrinking timescales under complex, potentially ill-defined conditions. The work argues that combining artificial and human intelligence can improve decision-making and decision support, enabling adaptable and efficient operations in rapidly changing environments. It leverages interactive machine learning where humans cooperate with algorithms, identifies gaps for extending these methods to complex C2 contexts, and defines three research focus areas to enable scalable interactive machine learning (SIML).

LanguageEnglish

CategoryResearch & Report

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Pages10

Reading Time25 min

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Introduction

Human-AI integration in C2

Proposed scalable interactive machine learning focus areas

Scalable Interactive Machine Learning for Future Command and Control arXiv :2402 .06501v2 [ cs .LG] 28 Mar 2024 Anna Madison∗1, Ellen Novoseller∗1, Vinicius G. Goecks∗1, Benjamin T. Files 1 , Nicholas Waytowich 1 , Alfred Yu 1 , Vernon J. Lawhern 1 , Steven Thurman 1 , Christopher Kelshaw2 , and Kaleb McDowell 1 1 Humans in Complex Systems, U.S. DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, USA 2 U.S. Mission Command Battle Lab, Futures Branch, Ft. Leavenworth, KS, USA Correspondence: Anna Madison, [anna.m.madison2.civ@army.mil](anna.m.madison2.civ@army.mil) Abstract—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-ofthe-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. Index Terms—Scalable Interactive Machine Learning, Artificial Intelligence, Human-AI Teaming, Command and Control I. INTRODUCTION Future warfare will place unprecedented and dramaticallyincreased demands on Command and Control (C2) 1 systems.2 Maintaining decision advantage over adversaries during multidomain operations (MDO) will require robust C2 decisionmaking at shorter timescales in more complex and potentially ill-defined situations. Dispersed, isolated, and mobile command post nodes, forces, and autonomous agents will *These three authors contributed equally to this work. This research was sponsored by the Army Research Laboratory and accomplished under Cooperative Agreement \#W911NF-23-2-0072 . This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-205-RSY – the ICMCIS, held in Koblenz, Germany, 23-24 April 2024 . 1The first “C” in C2, command, is the authoritative act of making decisions and ordering action, with key elements being authority, responsibility, decision-making, and leadership. The second “C” in C2, control, is defined as the act of monitoring and influencing command action through direction, feedback, information, and communications [1] . 2C2 systems specify arrangements of people, processes, networks, and command posts [2] . need to maintain unity of effort in the face of Denied, Degraded, Intermittent, and Limited (DDIL) communications, while integrating complex data streams into synchronized decision-making capabilities. Yet, current C2 processes are time-consuming and consist of linear sequences of steps, and thus may not adapt well to future battlefield challenges. To achieve decision dominance on the future battlefield, C2 systems will need to leverage recent breakthroughs in interactive machine learning3 and hybrid human-machine intelligence that indicate the potential for humans and AI to work together to leverage their respective strengths [3]–[5] . Thereis, however, no one-size-fits-all approach to human-machine integration. Metcalfe, Perelman et al. [6] introduce

FAQs

为什么未来作战需要更快、更稳健的指挥控制决策？

未来多域作战将对C2系统提出前所未有且显著增加的要求，需要在更短时间尺度上应对更复杂、可能不明确定义的态势，同时保持对对手的决策优势。

文中提出的可扩展交互式机器学习（SIML）核心思路是什么？

SIML利用交互式机器学习，让人类与机器学习算法协作来引导算法行为，从而在复杂环境下支持更高效、更可靠的决策与决策支持。

SIML的三项研究重点分别是什么？

重点包括：1）面向复杂动态情境的规划的人-机交互算法；2）通过优化角色、配置和校准信任来构建更具韧性的人-机团队；3）在不同潜在情境中实现算法与人-机团队的可扩展性。

Aug 2, 2026

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