Open AccessHearthstone Battleground: An AI Assistant with Monte Carlo Tree Search
Author(s) -
Namuunbadralt Zolboot,
Quinn Johnson,
Dakun Shen,
Alexander Redei
Publication year - 2022
Publication title -
epic series in computing
Language(s) - English
Resource type - Conference proceedings
ISSN - 2398-7340
DOI - 10.29007/mn6n
Subject(s) - monte carlo tree search , battle , sophistication , computer science , tree (set theory) , monte carlo method , set (abstract data type) , artificial intelligence , game tree , software , mathematical economics , game theory , sequential game , mathematics , art , combinatorics , history , statistics , programming language , archaeology , aesthetics
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%.