Abstract
In this work, we introduce a Monte-Carlo tree search (MCTS) approach for the game “Hearthstone: Heroes of Warcraft”, the most popular online Collectible Card Game, with 50 million players as of April 2016. In Hearthstone, players must deal with hidden information regarding the cards of the opponent, chance, and a complex game-play, which often requires sophisticated strategy. We argue that, in light of the challenges posed by the game (such as uncertainty and hidden information), Monte Carlo tree search offers an appealing alternative to existing AI players. Additionally, by enriching Monte Carlo tree search with a properly constructed heuristic, it is possible to introduce significant gains in performance. We illustrate through extensive validation the superior performance of our approach against ”vanilla” Monte Carlo tree search and the current state-of-the art AI for Hearthstone.