Fuzzy Logic–Based Vulnerability Analysis Methodology for Anticipating Adversary Threats to Ground Forces

To effectively conduct decision-centric warfare, having access to intelligent decision support technology is important, which can rapidly and accurately analyze threats on the battlefield using artificial intelligence (AI). Considering this, our current focus lies in the development of AI – command decision support for ground operation (AICDS-G) technology that can assist commanders in planning operations. This study outlines novel fuzzy logic–based methods and presents the experimental results obtained using these methods to assess the vulnerability of enemy threats in AICDS-G technology. In AICDS-G technology, we use artificial neural network models to evaluate enemy threat levels by identifying adversary behavior and predicting the probability of adversary threats. However, to conduct a practical threat assessment, it is also necessary to consider the vulnerability of friendly forces to enemy forces. To assess the vulnerability of friendly forces, we propose fuzzy logics using the capability and relationship variables for both forces. The vulnerability analysis of capability factors in variables such as firepower and the effective range, whereas the vulnerability analysis of relationship considers proximity variables related to the location and distance of forces. The proposed vulnerability analysis has been validated through experiments conducted on battlefield simulations. The resultant vulnerability data will be used to analyze enemy threats in AICDS-G technology.