Mission Impact Assessment (MIA) is critical for enhancing system effectiveness and ensuring mission success. This article presents Interdependent Mission Impact Assessment ( 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 Subjective Logic (SL)-based HGT model (SLHG), integrating 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 \(P_{ACC}\) . 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.

Subjective Bayesian networks (SBN) integrate Bayesian Networks (BN) with Subjective Logic, enabling the representation of second-order uncertainty, denoting the uncertainty surrounding the probability distribution of an event. Although prior research predominantly centers on exact inference within the SBN framework, there is a notable dearth of exploration into the realm of approximate inference in SBN. Our work is specifically geared towards addressing this gap, focusing on the application of diverse sampling methodologies (i.e., forward and Gibbs sampling) for approximate inference in SBN. The primary contribution of this work lies not only in the introduction of approximate inference in SBN but also in the formulation of an “anytime” SBN inference algorithm. This implies that a best inference estimate can be obtained at any given moment, given trade-offs in the precision. Moreover, the allocation of computational resources is a customizable and potentially optimizable process. Through a rigorous series of experiments, we empirically demonstrate that the number of iterations to convergence decreases as we provide more samples for both forward and Gibbs sampling. Furthermore, we discover the difference between approximate and exact inference in belief ($\delta_{\text {belief }}$) and uncertainty ($\delta_{\text {uncertainty }}$) mass of subjective opinion becomes more unpredictable as the error gets large in BN probability. Lastly, in our experiments, we demonstrate the number of BN samples has a greater impact on $\delta_{\text {belief }}$ than the number of SBN iterations. These findings indicate that the family of greedy algorithms (based on local graded changes - such as gradients) can be a promising approach for finding optimal allocations of computational resources in this framework. The software assets produced and used in this work will be made available as an open source Python library.

Subjective Bayesian networks (SBN) combine Bayesian Networks (BN) with Subjective Logic in order to express the second-order uncertainty (i.e., the uncertainty about a probability distribution of an event – as opposed to the uncertainty about the event itself). While SBNs provide a strong formalism for treating the uncertainty in a higher level, the literature lacks support for extensive software implementations focused on compatibility with current software solutions or standards. Our work explores the structural congruence between BN and SBN (in terms of software data structure) and a semantic bijection between Subjective Logic opinions to Dirichlet distributions to introduce a SBN reasoning framework that targets on effectively reusing the existing BN solutions. Particularly, we developed two inference algorithms that apply a Monte Carlo method to existing BN inference algorithms (we chose the Junction Tree algorithm, for test), respectively for batch and interactive SBN reasoning. A method for translating an evidence in SBN to uncertain (virtual) evidence in BN is also presented. The main contribution of this paper is the introduction of a simple, yet flexible empirical estimation method and a software architecture that virtually adapts any BN inference algorithm to an approximate computational inference framework for SBN. We also developed a Java component to demonstrate the reusability, and we developed a case study of Mission Impact Assessment of Unmanned Aerial Vehicles transporting critical items between hospitals in order to illustrate the applicability in a knowledge engineering process.