Evolving the hearthstone meta

Fernando De Mesentier Silva, Rodrigo Canaan, Scott Lee, Matthew C. Fontaine, Julian Togelius, Amy K. Hoover

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationIEEE Conference on Games 2019, CoG 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728118840
DOIs
StatePublished - Aug 2019
Event2019 IEEE Conference on Games, CoG 2019 - London, United Kingdom
Duration: Aug 20 2019Aug 23 2019

Publication series

NameIEEE Conference on Computatonal Intelligence and Games, CIG
Volume2019-August
ISSN (Print)2325-4270
ISSN (Electronic)2325-4289

Conference

Conference2019 IEEE Conference on Games, CoG 2019
CountryUnited Kingdom
CityLondon
Period8/20/198/23/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

Keywords

  • Evolutionary Algorithm
  • Game Balancing
  • Hearthstone
  • Multi-Objective Optimization

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