Evolving scout agents for military simulations

Simulations play an increasingly significant role in training and preparing the military, particularly in environments with constrained budgets. Unfortunately, in most cases a small number of people must control a large number of simulated vehicles and soldiers. This often leads to micromanagement of computer-controlled forces in order to get them to exhibit the human-like characteristics of an enemy force. This thesis uses Neuroevolution of Augmenting Topologies (NEAT) to train neural networks to perform the role of scouts which analyze the terrain and decide where to place themselves to best observe the enemy forces. The main attribute that the scout agents consider is a vapor flow rate from the enemy starting location to their intended objective, which according to previous studies indicates likely chokepoints along the enemy route. This thesis experiments with different configurations of sensors and fitness functions in order to maximize how much of the enemy team is spotted over the course of the scenario. The results show that these agents perform better than randomly placed scouts and better than scouts deployed using heuristics in many situations, although not consistently so. Evolutionary optimization of scout agents using vapor flow is thus a promising approach for developing autonomous scout agents in military simulations.