iMIA: Assessing Mission Risk in Uncertain, Interdependent AI Systems

Abstract

Mission Impact Assessment (MIA) is critical for enhancing system effectiveness and ensuring mission success. This paper presents iMIA, an interdependent MIA framework that models relationships among mission components and enables probabilistic reasoning under uncertainty. Designed for AI-driven mission systems operating in dynamic, low-data, or poorly observable environments, iMIA addresses the limitations of traditional methods that often rely on overly confident assumptions about adversary behavior. While conventional hypergame theory (HGT) captures perceptual uncertainty from asymmetric or inaccurate views, it overlooks epistemic uncertainty arising from limited knowledge. To bridge this gap, we introduce a hybrid SL-based HGT model (SLHG), integrating Subjective Logic (SL) to represent epistemic uncertainty and HGT to account for misperceptions. This integration supports informed decision-making under both uncertain strategy beliefs and divergent environmental views. iMIA evaluates mission impact using multidimensional system quality metrics, security, trust, resilience, and agility, across diverse attacker-defender interactions. It identifies critical nodes influencing mission outcomes and quantifies performance gains from asset capacity reinforcement and asset vulnerability mitigation. Applied to a vehicle-assisted AI-based mission system, iMIA with SLHG improves performance by 16% in ASR, 20% in MTBF, 11% in TSA, and 14% in PACC. Designed for incremental development, iMIA supports continuous feedback and iterative refinement. Our results show that feedback-driven adjustments improve overall system performance by up to 18% in the accuracy performance.