Optimizing Hearthstone agents using an evolutionary algorithm
| Autor: | Carlos Cotta, Antonio J. Fernández-Leiva, Alberto Tonda, Pablo García-Sánchez |
|---|---|
| Přispěvatelé: | Dept. of Computer Architecture and Computer Technology, Universidad de Granada (UGR), Génie et Microbiologie des Procédés Alimentaires (GMPA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, ETSI Informática, Universidad de Málaga [Málaga], Universidad de Málaga [Málaga] = University of Málaga [Málaga] |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Information Systems and Management
Computer science Process (engineering) business.industry Evolutionary algorithm Computational intelligence 02 engineering and technology Outcome (game theory) Field (computer science) Evolutionary computation Management Information Systems [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Tree (data structure) [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Artificial Intelligence 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Video game Software ComputingMilieux_MISCELLANEOUS |
| Zdroj: | Knowledge-Based Systems Knowledge-Based Systems, Elsevier, 2020, 188, pp.105032. ⟨10.1016/j.knosys.2019.105032⟩ |
| ISSN: | 0950-7051 1872-7409 |
| DOI: | 10.1016/j.knosys.2019.105032⟩ |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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