An Online Learning Framework For Intelligence Collection In Uncertain Environments
Abstract
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 are source-constrained environment. We devise an online optimization framework, accompanied by rigorous analysis and comprehensive numerical experiments, that sheds light on this real-world problem.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2021
- Accession Number
- AD1164220
Entities
People
- Amit Carmeli
Organizations
- Naval Postgraduate School