Online Learning Approach to Predict Value of Information

Abstract

The modern battlefield environment presents commanders and analysts with an overwhelming amount of information. Only portions of this information are useful at any given moment, often requiring human intervention to parse out what is meaningful and what is not. In an environment where every second counts, methods for accelerating the presentation of only useful information to decision makers is critical. Machine learning is widely used to predict patterns and outcomes in a variety of applications where data structures are complex and high-dimensional. Supervised learning is a traditional machine learning method wherein the algorithm is trained on a large set of data before performing predictions. On the other hand, online learning is a machine learning technique wherein the algorithm learns incrementally or whenever new data and feedback are available. This work seeks to develop a proof of concept for predicting the utility value of incoming sensor data for a user via an online learning method. It also investigates changes in model performance with respect to hyperparameter configuration and the conditions that cause a user to accept that piece of information on each trial presentation via simulated experiments.