Enhancing Tactical Military Mission Execution through Human-AI Collaboration: A View on Air Battle Management Systems

Military mission planning spans a broad spectrum, encompassing strategic, operational, and tactical dimensions, with timeframes ranging from months to seconds. The integration of Artificial Intelligence (AI) offers great promise to enhance operational speed and effectiveness. This paper explores the intricacies of improving tactical military mission execution through the collaboration of human and AI systems. For this, we derive three distinct categories of human decision-making within mission execution: Level 1 - course of action refinement, Level 2 - task planning and scheduling, and Level 3 - tactical behavior generation. We analyze two AI-enhanced architectural approaches: A centralized Air Battle Management System (ABMS), which optimizes mission execution by providing a holistic operational view, and a decentralized, user-centric ABMS, which augments human operators with intelligent agents to improve the speed and quality of decision-making. While empirical implementation and quantitative analysis are beyond our scope, we provide a theoretical framework and rationale for these AI-enhanced architectural approaches. We advocate for the user-centric system and present our work on the software agents. Furthermore, we conceptualize a hybrid planning system for Level 2 decisions, designed to address the scalability challenges.