Machine Learning and Feature Selection for Videogame Beta Testing

This work presents the integration of Machine Learning techniques in the beta testing phase of Heartbot - Escape, a commercial video game, in order to help the game designer in the identification of the game parameters and player characteristics that have the most influence on metrics such as entertainment and difficulty. The game was analysed to define the actual input features and several classification outputs, data collection was designed and performed and data were then analysed