Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 127–130 (2017)
Abstract. The following article is created as a result of AAIA'17 Data Mining Challenge: Helping AI to Play Hearthstone. The Challenge goal was to correctly predict which bot will win a bot-vs-bot Hearthstone match based on what was known at a given time moment. Hearthstone is an online two-players card game with imperfect information (unlike chess and go, and alike poker), where the goal of one player is to defeat his/hers opponent by decreasing his/hers ''life points'' to zero (while not allowing opponent to do the same to oneself). Two main challenges were present: transformation of hierarchically structured data into a two-dimensional matrix, and dealing with Non-IID data (certain cards were present only in test data). A way how to successfully cope with those complications while using state-of-the-art machine learning algorithms (e.g. Microsoft's LightGBM) is presented.
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