Hearthstone battleground: An ai assistant with monte carlo tree search

Namuunbadralt Zolboot, Quinn Johnson, Dakun Shen, Alexander Redei

Research output: Contribution to journalConference articlepeer-review


We are in the golden age of AI. Developing AI software for computer games is one of the most exciting trends of today’s day and age. Recently games like Hearthstone Battlegrounds have captivated millions of players due to it’s sophistication, with an infinite number of unique interactions that can occur in the game. In this research, a Monte-Carlo simulation was built to help players achieve higher ranks. This was achieved through a learned simulation which was trained against a top Hearthstone Battleground player’s historic win. In our experiment, we collected 3 data sets from strategic Hearthstone Battleground games. Each data set includes 6 turns of battle phases, 42 minions for battle boards, and 22 minions for Bob’s tavern. The evaluation demonstrated that the AI assistant achieved better performance — loosing on average only 9.56% of turns vs 26.26% for the experienced Hearthstone Battleground players, and winning 56% vs 46.91%.

Original languageEnglish
Pages (from-to)131-140
Number of pages10
JournalEPiC Series in Computing
StatePublished - 2022
Event37th International Conference on Computers and Their Applications, CATA 2022 - Virtual, Online
Duration: Mar 21 2022Mar 23 2022


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