Regression networks for robust win-rates predictions of AI gaming bots

Designing a robust and adaptable Artificial Intelligence (AI) opponent in a computer game would ensure the game continues to challenge, immerse and excite the players at any stage. The outcomes of card based games such as 'Heartstone: Heroes of Warcraft', aside the player skills, heavily depend on the initial composition of player card decks. To evaluate this impact we have developed a new robust regression network in a context of the AAIA Data Mining Competition 2018, which tries to predict the average win-rates of the specific combinations of bot-player and card decks. Our network is composed of 2 levels: the entry level with an array of finely optimized state of the art regression models including Extreme Learning Machines (ELM), Extreme Gradient Boosted decision tree (XGBOOST), and Least Absolute Shrinkage and Selection Operator (LASSO) regression trained via supervised learning on the labeled training dataset; and just a single ELM at the 2^nd level installed to learn to correct the predictions from the 1^st level. The final solution received the root of the mean squared error (RMSE) of just 5.65% and scored the 2^nd place in AAIA'2018 competition. This paper also presents two other runner-up models receiving RMSE of 5.7% and 5.86%, scoring the 4^th and the 6^th place respectively.