Hybrid AI Model for Proportionality Assessment in Military Operations

Abstract

In recent years, Artificial Intelligence (AI) has revolutionized the military domain and in particular the planning, execution, and assessment of military operations, leading to the development of advanced decision support systems. In this context, this research introduces a novel hybrid AI model for proportionality assessment in military operations, merging the advantages of artificial neural networks with fuzzy logic to create a robust and adaptable system. This approach combines the learning capabilities and pattern recognition strengths of neural networks with the ability of fuzzy logic to handle uncertainty and linguistic variables. In this way, the model addresses the complex challenge of estimating collateral damage and military advantage in dynamic operational environments and further proposes proportionality assessment decisions. Experimental results demonstrate that this intelligent approach contributes to existing models in both accuracy and explainability terms. Moreover, the model is adaptable to diverse scenarios and provides clear, interpretable results, aspects that are crucial for military decision-makers. By bridging the gap between data-driven learning and expert knowledge representation, this research contributes to the development of more ethical and legally compliant AI solutions for military operations, particularly in the critical domain of proportionality assessment in targeting decisions.