Monte Carlo Tree Search Experiments in Hearthstone

André Santos, Pedro A. Santos, Francisco S. Melo

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review


In this paper, we introduce a Monte-Carlo tree search (MCTS) approach for the game “Hearthstone: Heroes of Warcraft”. 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 MCTS with a properly constructed heuristic, it is possible to introduce significant gains in performance.We illustrate through extensive empirical validation the superior performance of our approach against vanilla MCTS and the current state-of-the art AI for Hearthstone.
Original languageEnglish
Title of host publication2017 IEEE Conference on Computational Intelligence and Games (CIG)
Place of PublicationNew York, NY
ISBN (Electronic)978-1-5386-3233-8
Publication statusPublished - 2017
Externally publishedYes
Event2017 Conference on Computational Intelligence in Games - New York University, New York, United States
Duration: 22 Aug 201725 Aug 2017


Conference2017 Conference on Computational Intelligence in Games
Abbreviated titleCiG2017
Country/TerritoryUnited States
CityNew York
Internet address


  • Monte Carlo Tree Search
  • Artificial intelligence for games
  • Hearthstone


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