Showing papers by "Andrew S. Gordon published in 2020"

Recognizing Multiplayer Behaviors Using Synthetic Training Data

Abstract: Accurate recognition of group behaviors is essential to the design of engaging networked multiplayer games. However, contemporary data-driven machine learning solutions are difficult to apply during the game development process, given that no authentic gameplay data is yet available for use as training data. In this paper, we investigate the use of synthetic training data, i.e., gameplay data that is generated by AI-controlled agent teams programmed to perform each of the behaviors to be recognized in groups of human players. The particular task we focus on is to recognize group movement formations in player-controlled avatars in a realistic virtual world. We choose five typical military team movement patterns for the formation recognition task and train machine learning models using procedurally generated unit trajectories as training data. The experiments were conducted using ResNet and EfficientNet, which are two popular convolutional neural network architectures for image classifications. The synthetic data is augmented by creating variations in image rotation, unit spacing, team size, and positional perturbations to bridge the gap between synthetic and human gameplay data. We demonstrate that high-accuracy behavior recognition can be achieved using deep neural networks by applying the aforementioned data augmentation methods to simulated gameplay data.