AI 기반 M&S를 활용한 전장 환경 분석의 정책적 함의

Abstract

The rapidly evolving future battlefield, characterized by the convergence of hyper-connectivity, hyper-intelligence, and hyper-reality technologies, is increasingly complex and uncertain. This study explores methods for optimizing the battlefield environment using Artificial Intelligence (AI)-based Modeling & Simulation (M&S) to address these challenges and overcome the limitations of traditional defense M&S. The research begins with an in-depth examination of future battlefield characteristics, the understanding of Al-based M&S, and complex systems theory. It then proposes key optimization areas for Al-based M&S, including the refinement of big data-driven modeling, the automation and diversification of intelligent scenario operations, and real-time battle command and decision-making support. Furthermore, the study suggests concrete strategies such as establishing robust big data governance, strengthening integration with the CJADC2 (Combined Joint All-Domain Command and Control) system, and ensuring the agility and reliability of Al-based M&S. Ultimately, this research emphasizes that Al-based M&S will contribute to future defense policy formulation, enhanced efficiency in force augmentation, and the innovation of military education and training systems, thereby providing practical solutions for effectively optimizing complex battlefield environments.