In complex operational environments, signal ambiguity and false alarms make task allocation for multiple unmanned aerial vehicles (UAVs) challenging. To address this problem, we propose the Bayesian Rolling Information-Theoretic Reconnaissance Action Assignment (RITRA) framework. RITRA integrates Bayesian belief tracking, expected information gain (EIG), and a cooperative criticality model based on Shapley values in a unified receding-horizon strategy. By assessing reconnaissance and intervention utilities for each UAV–hazard pair, RITRA casts mission planning as a dynamic optimization problem. We use an enhanced Hungarian algorithm with idle states, allowing UAVs to remain on standby when no deployment has positive expected value. Ablation results indicate that combining Bayesian tracking, active information collection, and risk-aware execution contributes to robust performance under the evaluated uncertainty conditions. In the 200-run paired Monte Carlo stress test, RITRA achieved a mean mission utility of 9.4362, a normalized mission score of 0.7890, and 0.245 false deployments per run. Against the best-performing baseline, a CBBA-based allocation method, RITRA achieved a mean paired mission-utility gain of 1.4925. On publicly released dynamic MRTA instances augmented with false-alarm uncertainty, RITRA achieved the highest aggregate mission utility, normalized mission score, and mission accuracy among the compared controllers, providing additional evidence of its effectiveness. These results indicate that RITRA supports risk-aware multi-UAV allocation under the evaluated uncertain conditions.

Observational data with massive sample sizes are often distributed on many local machines. From an experimental design perspective, investigators often desire to identify the effect of new treatments (even ML algorithms) on many blocks of experimental data. With time requirements or budget constraints, assigning all treatments to each block is not always feasible. This creates incomplete responses with respect to a randomized complete block design (RCBD). These incomplete responses are missing by design. However, whether they can be estimated with missing imputation methods is not well understood. Thus, it is challenging to correctly identify the treatment effects with missing data. To this end, this paper provides a method for imputation and analysis of the responses with missing data. The proposed method consists of three steps: Reconstruction, Imputation, and ‘Complete’-data Analysis (RICA). The incomplete responses are imputed with the expectation-maximization (EM) algorithm. The RCBD model is then fitted by the resulting dataset. The identifiability result suggests that the missing may be nonignorable for each block, but the whole data of an incomplete design are missing by design when the design is balanced. Theoretical results on relative efficiency also inform us when the missingness should be imputed for incomplete designs with the role of balanced variance. Applications on real-world data verify the efficacy of this method.