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Scalable Interactive Machine Learning for Future Command and Control

Anna Madison, Ellen Novoseller, Vinicius G. Goecks, Benjamin T. Files, Nicholas Waytowich, Alfred Yu, Vernon J. Lawhern, Steven Thurman, Christopher Kelshaw, Kaleb McDowell

TL;DR

The paper tackles the challenge of enabling robust, rapid decision-making in future C2 contexts by proposing Scalable Interactive Machine Learning (SIML) as a framework for human-AI collaboration. It identifies three core research focus areas—planning with human-AI interaction, resilient and efficient human-AI teams, and scalable deployment across time, personnel, hierarchies, and problem scopes—along with concrete open questions and avenues for advancing SIML in real-world C2 environments. By detailing interaction modalities, multi-agent coordination, hierarchical structures, explainability, and scalability, the authors map a roadmap for integrating human insight with AI capabilities to compress the military decision-making cycle and maintain coordination under DDIL conditions. The work emphasizes practical considerations, such as edge-computing viability, interoperability (e.g., NATO standards), and data/model management, to bridge the gap between research and field deployment with a horizon extending toward 2040. Overall, SIML is presented as a critical enabler for adaptive, trustworthy, and scalable C2 decision-support in complex multi-domain operations.

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-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.

Scalable Interactive Machine Learning for Future Command and Control

TL;DR

The paper tackles the challenge of enabling robust, rapid decision-making in future C2 contexts by proposing Scalable Interactive Machine Learning (SIML) as a framework for human-AI collaboration. It identifies three core research focus areas—planning with human-AI interaction, resilient and efficient human-AI teams, and scalable deployment across time, personnel, hierarchies, and problem scopes—along with concrete open questions and avenues for advancing SIML in real-world C2 environments. By detailing interaction modalities, multi-agent coordination, hierarchical structures, explainability, and scalability, the authors map a roadmap for integrating human insight with AI capabilities to compress the military decision-making cycle and maintain coordination under DDIL conditions. The work emphasizes practical considerations, such as edge-computing viability, interoperability (e.g., NATO standards), and data/model management, to bridge the gap between research and field deployment with a horizon extending toward 2040. Overall, SIML is presented as a critical enabler for adaptive, trustworthy, and scalable C2 decision-support in complex multi-domain operations.

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-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.
Paper Structure (17 sections, 1 figure)

This paper contains 17 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Planning in Complex and Dynamic Environments with Interactive Machine Learning
  3. Methods for Humans to Guide Learning and Planning Processes via a Range of Interaction Modalities
  4. Methods for Multiple Humans and Multiple AI Agents to Interact in Complex and Dynamic Environments
  5. Human-AI Interaction Methods in which Humans Interact at Multiple Levels in a Hierarchy
  6. Human-AI Interaction Techniques Robust to Limited Communication and Imperfect Information Scenarios
  7. Human-AI Interaction Techniques for Leveraging Adapting Databases of Human-Generated Data
  8. Effective and Resilient Human-AI Teams for Complex, Dynamic Tasks
  9. Defining Resilient Human-AI Roles and Configurations
  10. Effective Human-AI Partnerships through Selecting and Training Optimal Human Partners
  11. Communication through Explainable AI and Shared Mental Models
  12. Flexible and Scalable Decision-Making with Interactive Machine Learning
  13. Temporal Scalability
  14. Human-AI Interaction Scalability
  15. Hierarchical Scalability
  16. ...and 2 more sections

Figures (1)

  • Figure 1: Scalable Interactive Machine Learning (SIML) research focus areas. We propose three research focus areas in SIML to achieve the envisioned C2 developments: 1) developing human-AI interaction algorithms for planning in complex and dynamic environments; 2) fostering resilient human-AI teaming, for instance optimizing team configurations and calibrated trust; and 3) scaling methods developed in the first two research areas to succeed across anticipated future battlefield scenarios.