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\title{Artificial Intelligence in Combat Decision-making: Weapon Target
Assignment via Reinforcement Learning and Graph Neural Networks}



\author[1]{Seung Heon Oh}%
\author[1]{Geon Woong Byeon}%
\author[1]{Young In Cho}%
\author[1]{Seungmin Kwon}%
\author[1]{Jong Hun Woo}%
\affil[1]{Affiliation not available}%


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  \date{May 01, 2026}


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\begin{abstract}
Selecting a threat to attack is one of the most important decisions on
the battlefield. The decision problem is represented as a Weapon-Target
Assignment problem (WTA) problem. In the previous studies, dynamic
programming, linear programming, metaheuristics, and heuristic methods
have been applied to solve this problem. However, previous studies have
been limited by oversimplified-model, computational burden, lack of
adaptability to disruptive events, and recalculation when the problem
size changes. To overcome these limitations, this study aims to solve
WTA by using reinforcement learning and graph neural networks. The
proposed method has high practicality by reflecting the real-world

decision-making framework, OODA-loop (Observe-Orient-Decide).
Experiments are conducted in various environments, and the effectiveness
of the proposed method is demonstrated by comparing it with existing
heuristic and meta-heuristic methodologies. The proposed method
introduces a groundbreaking methodology for intelligent decision-making
in tactical command and control traditionally considered exclusive to
human-expert.%
\end{abstract}%



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