Enemy Location Prediction in Naval Combat Using Deep Learning

Abstract

The immensely complex realm of naval warfare presents challenges for which machine learning is uniquely suited. In this paper, we present a machine learning model to predict the location of unseen enemy ships in real time, based on the current known positions of other ships on the battlefield. More broadly, this research seeks to validate the ability of basic machine learning algorithms to make meaningful classifications and predictions of simulated adversarial naval behavior. Using gameplay data from World of Warships, we deployed an artificial neural network (ANN) model and a Random Forest model to serve as prediction engines that update as the battle progresses, overlaying probabilities over the battlefield map indicating the likelihood of the unseen ship being at each location. The models were trained and tested on gameplay data from a World of Warships tournament in which former naval officers served as commanders of competing fleets. This tournament structure ensured cohesive and coordinated naval fleet behavior, yielding data similar to that seen in real-world naval combat and increasing the applicability of our model. Both the Random Forest and ANN model were successful in their predictive capabilities, with the ANN proving to be the best method.