Optimizing Hearthstone agents using an evolutionary algorithm

Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. This game genre allows researchers to deal with hidden information, uncertainty and planning, among other aspects. This paper prop...

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Bibliographic Details
Published in:Knowledge-based systems 2020-01, Vol.188, p.105032, Article 105032
Main Authors: García-Sánchez, Pablo, Tonda, Alberto, Fernández-Leiva, Antonio J., Cotta, Carlos
Format: Article
Language:English
Subjects:
Agents (artificial intelligence)
Artificial Intelligence
Card games
Collectible card games
Computer & video games
Computer Science
Decision making
Evolution strategy
Evolutionary algorithms
Games
Genetic algorithms
Hearthstone
Machine Learning
Monte Carlo simulation
Optimization
Videogames
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Summary:Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. This game genre allows researchers to deal with hidden information, uncertainty and planning, among other aspects. This paper proposes the use of evolutionary algorithms (EAs) to develop agents who play a card game, Hearthstone, by optimizing a data-driven decision-making mechanism that takes into account all the elements currently in play. Agents feature self-learning by means of a competitive coevolutionary training approach, whereby no external sparring element defined by the user is required for the optimization process. One of the agents developed through the proposed approach was runner-up (best 6%) in an international Hearthstone Artificial Intelligence (AI) competition. Our proposal performed remarkably well, even when it faced state-of-the-art techniques that attempted to take into account future game states, such as Monte-Carlo Tree search. This outcome shows how evolutionary computation could represent a considerable advantage in developing AIs for collectible card games such as Hearthstone.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2019.105032