Hearthstone Battleground: An AI Assistant with Monte Carlo Tree Search

10 pages•Published: March 18, 2022

Namuunbadralt Zolboot, Quinn Johnson, Dakun Shen and Alexander Redei

Abstract

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 Bat- tlegrounds 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 Bat- tleground 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 assis- tant 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%.

Keyphrases: ai, hearthstone battleground simulator, monte carlo tree search

In: Bidyut Gupta, Ajay Bandi and Mohammad Hossain (editors). Proceedings of 37th International Conference on Computers and Their Applications, vol 82, pages 131-140.

Links:	https://easychair.org/publications/paper/Kftf
	https://doi.org/10.29007/mn6n

BibTeX entry

@inproceedings{CATA2022:Hearthstone_Battleground_AI_Assistant,
  author    = {Namuunbadralt Zolboot and Quinn Johnson and Dakun Shen and Alexander Redei},
  title     = {Hearthstone Battleground: An AI Assistant with Monte Carlo Tree Search},
  booktitle = {Proceedings of 37th International Conference on Computers and Their Applications},
  editor    = {Bidyut Gupta and Ajay Bandi and Mohammad Hossain},
  series    = {EPiC Series in Computing},
  volume    = {82},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/Kftf},
  doi       = {10.29007/mn6n},
  pages     = {131-140},
  year      = {2022}}

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