TY - GEN
T1 - Evolving the hearthstone meta
AU - Mesentier Silva, Fernando De
AU - Canaan, Rodrigo
AU - Lee, Scott
AU - Fontaine, Matthew C.
AU - Togelius, Julian
AU - Hoover, Amy K.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Balancing an ever growing strategic game of high complexity, such as Hearthstone is a complex task. The target of making strategies diverse and customizable results in a delicate intricate system. Tuning over 2000 cards to generate the desired outcome without disrupting the existing environment becomes a laborious challenge. In this paper, we discuss the impacts that changes to existing cards can have on strategy in Hearthstone. By analyzing the win rate on match-ups across different decks, being played by different strategies, we propose to compare their performance before and after changes are made to improve or worsen different cards. Then, using an evolutionary algorithm, we search for a combination of changes to the card attributes that cause the decks to approach equal, 50% win rates. We then expand our evolutionary algorithm to a multi-objective solution to search for this result, while making the minimum amount of changes, and as a consequence disruption, to the existing cards. Lastly, we propose and evaluate metrics to serve as heuristics with which to decide which cards to target with balance changes.
AB - Balancing an ever growing strategic game of high complexity, such as Hearthstone is a complex task. The target of making strategies diverse and customizable results in a delicate intricate system. Tuning over 2000 cards to generate the desired outcome without disrupting the existing environment becomes a laborious challenge. In this paper, we discuss the impacts that changes to existing cards can have on strategy in Hearthstone. By analyzing the win rate on match-ups across different decks, being played by different strategies, we propose to compare their performance before and after changes are made to improve or worsen different cards. Then, using an evolutionary algorithm, we search for a combination of changes to the card attributes that cause the decks to approach equal, 50% win rates. We then expand our evolutionary algorithm to a multi-objective solution to search for this result, while making the minimum amount of changes, and as a consequence disruption, to the existing cards. Lastly, we propose and evaluate metrics to serve as heuristics with which to decide which cards to target with balance changes.
KW - Evolutionary Algorithm
KW - Game Balancing
KW - Hearthstone
KW - Multi-Objective Optimization
UR - http://www.scopus.com/inward/record.url?scp=85073105748&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073105748&partnerID=8YFLogxK
U2 - 10.1109/CIG.2019.8847966
DO - 10.1109/CIG.2019.8847966
M3 - Conference contribution
AN - SCOPUS:85073105748
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - IEEE Conference on Games 2019, CoG 2019
PB - IEEE Computer Society
T2 - 2019 IEEE Conference on Games, CoG 2019
Y2 - 20 August 2019 through 23 August 2019
ER -