AI-Driven Human-Autonomy Teaming in Tactical Operations: Proposed Framework, Challenges, and Future Directions

• URL: http://arxiv.org/abs/2411.09788v1
• Date: Mon, 28 Oct 2024 15:05:16 GMT
• Title: AI-Driven Human-Autonomy Teaming in Tactical Operations: Proposed Framework, Challenges, and Future Directions
• Authors: Desta Haileselassie Hagos, Hassan El Alami, Danda B. Rawat,
• Abstract summary: Artificial Intelligence (AI) techniques are transforming tactical operations by augmenting human decision-making capabilities. This paper explores AI-driven Human-Autonomy Teaming (HAT) as a transformative approach. We propose a comprehensive framework that addresses the key components of AI-driven HAT.
• Score: 10.16399860867284
• License:
• Abstract: Artificial Intelligence (AI) techniques, particularly machine learning techniques, are rapidly transforming tactical operations by augmenting human decision-making capabilities. This paper explores AI-driven Human-Autonomy Teaming (HAT) as a transformative approach, focusing on how it empowers human decision-making in complex environments. While trust and explainability continue to pose significant challenges, our exploration focuses on the potential of AI-driven HAT to transform tactical operations. By improving situational awareness and supporting more informed decision-making, AI-driven HAT can enhance the effectiveness and safety of such operations. To this end, we propose a comprehensive framework that addresses the key components of AI-driven HAT, including trust and transparency, optimal function allocation between humans and AI, situational awareness, and ethical considerations. The proposed framework can serve as a foundation for future research and development in the field. By identifying and discussing critical research challenges and knowledge gaps in this framework, our work aims to guide the advancement of AI-driven HAT for optimizing tactical operations. We emphasize the importance of developing scalable and ethical AI-driven HAT systems that ensure seamless human-machine collaboration, prioritize ethical considerations, enhance model transparency through Explainable AI (XAI) techniques, and effectively manage the cognitive load of human operators.

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