Predicting win-rates of hearthstone decks: Models and features that won AAIA'2018 data mining challenge

Abstract

Success of many computer games depends on designing a robust and adaptable AI opponent that would ensure the games continue to challenge, immerse and excite the players at any stage. The outcomes of card based games like 'Heartstone: Heros of Warcraft', aside the player skills heavily depend on the initial composition of player card decks. To evaluate this impact we have developed an ensemble prediction model that tries to predict the average win-rates of the specific combination of bot-player and card decks. Our ensemble model consists of three sub-models: two Logistic Regression models and one Deep Learning model. The models are trained with both provided data and additional data about the cards, their health, attack power and cost. To avoid overfitting, we employ a trick to generate predictions for all possible combinations of opponent players and decks and obtain the result as the average of all these predictions.