Similarity Model using Gradient Images to Compare Human and AI Agents

The objective is to determine how close an Artificial Intelligence agent is, in comparison to a human player, using only game play images. Identifying Artificial Intelligence agents during game play is typically done through the analysis and collection of bio-metric data, such as keyboard, mouse and other controller interfaces. This document presents a model of an Auto Encoder architecture, with Long Short Term Memory layers. Gradient and Non-Augmented Images have been evaluated and compared. Three distinct personalities of agents are evaluated, human players, a trained Artificial Intelligence agent, and a basic Artificial Intelligence agent. Through testing the Gradient Image augmentation sets present promising results, with the model successfully identifying the human as the closest similarity to the baseline, followed by the trained Artificial Intelligence agent.