AN ONLINE LEARNING FRAMEWORK FOR INTELLIGENCE COLLECTION IN UNCERTAIN ENVIRONMENTS

The process of creating an intelligence status report requires a continuous collection of intelligence from various sources of varying inaccuracies and reliability. Consequently, managing many intelligence sources is not only a costly operation to establish but also to maintain continuously. Our objective is to use the Multi-armed Bandit (MAB) framework to model intelligence collection. The proposed framework generalizes the classical MAB model by accounting for censoring in sampled observations in a resource-constrained environment. We devise an online optimization framework, accompanied by rigorous analysis and comprehensive numerical experiments, that sheds light on this real-world problem.Seren, Israel Defence ForcesApproved for public release. Distribution is unlimited