Automated feature engineering for prediction of victories in online computer games
Dymitr Ruta, Ling Cen, Ming Liu, Quang Hieu Vu · 2021 IEEE International Conference on Big Data (Big Data) · 2021
An accurate evaluation of player-win likelihoods during online games is critical for controlling the attractiveness and immersion of the gameplay, especially if played against an AI bot. Predicting game victories is a very challenging problem heavily depends on the complexity and the stage of the game. With the multitude of ways and formats that the players, the gameplay, individual state and history can be encoded and utilized by machine learning (ML) models, it is fascinating to take part in the IEEE Big Data 2021 Cup and explore which data representations, feature engineering methods and prediction models work better than others and what levels of predictability they can achieve in the specific use case of predicting victories in Tactical Troops: Anthracite Shift online computer game. Our explorations throughout the game and a high 4th place in the Cup’s final indicate that a careful and comprehensive feature engineering methodology numerically capturing the last state of the game paired with the variants of the gradient boosting models offer a robust and competitive solution for this challenge.