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This paper summarizes the AAIA'17 Data Mining Challenge: Helping AI to PlayHearthstone which was held between March 23, and May 15, 2017 at the KnowledgePit platform. We briefly describe the scope and background of this competitionin the context of a more general project related to the development of an AIengine for video games, called Grail. We also discuss the outcomes of thischallenge and demonstrate how predictive models for the assessment of player'swinning chances can be utilized in a construction of an intelligent agent forplaying Hearthstone. Finally, we show a few selected machine learningapproaches for modeling state and action values in Hearthstone. We provideevaluation for a few promising solutions that may be used to create moreadvanced types of agents, especially in conjunction with Monte Carlo TreeSearch algorithms.

Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge