Developing Competitive Strategies for Legends of Runeterra using Genetic Algorithm
Resumo
Card games have long served as recreational tools across various cultures. This genre has also followed the trends of the digital gaming industry, with numerous games now featuring competitive scenes with substantial prizes, such as Legends of Runeterra. This paper proposes an automatic generator for competitive deck lineups in Legends of Runeterra, assessing extensive match databases using a Genetic Algorithm that evaluates candidate solutions based on three different generation strategies. This study introduces an evaluation function and presents empirical results concerning the effectiveness and efficiency of the approach. The findings indicate that the proposed method is useful and representative in a competitive context for all three addressed generation strategies.
Palavras-chave:
Legends of Runeterra, Digital Card Games, Genetic Algorithm, Strategy Composition
Referências
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Chia, H.-C., Yeh, T.-S., e Chiang, T.-C. (2020). Designing card game strategies with genetic programming and monte-carlo tree search: A case study of hearthstone. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pages 2351–2358.
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Games, R. (2024). Legends of runeterra.
García-Sánchez, P., Tonda, A., Squillero, G., Mora, A., e Merelo, J. J. (2016). Evolutionary deckbuilding in hearthstone. In 2016 IEEE Conference on Computational Intelligence and Games (CIG), pages 1–8.
K. Z. Zamli, B. Y. A. e Kendall, G. (2016). A tabu search hyper-heuristic strategy for t-way test suite generation. Applied Soft Computing, vol. 44.
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Mitsis, K., Kalafatis, E., Zarkogianni, K., Mourkousis, G., e Nikita, K. S. (2020). Procedural content generation based on a genetic algorithm in a serious game for obstructive sleep apnea. In 2020 IEEE Conference on Games (CoG), pages 694–697.
Ward, C. D. e Cowling, P. I. (2009). Monte carlo search applied to card selection in magic: The gathering. In 2009 IEEE Symposium on Computational Intelligence and Games, pages 9–16.
Weise, T. (2008). Global optimization algorithms – theory and application, 1st ed.
Yang, Y., Yeh, T., e Chiang, T. (2021). Deck building in collectible card games using genetic algorithms: A case study of legends of code and magic. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pages 01–07.
Betley, J., Sztyber, A., e Witkowski, A. (2018). Predicting winrate of hearthstone decks using their archetypes. In 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), pages 193–196.
Chen, Z., Amato, C., Nguyen, T.-H. D., Cooper, S., Sun, Y., e El-Nasr, M. S. (2018). Q-deckrec: A fast deck recommendation system for collectible card games. In 2018 IEEE Conference on Computational Intelligence and Games (CIG), pages 1–8.
Chia, H.-C., Yeh, T.-S., e Chiang, T.-C. (2020). Designing card game strategies with genetic programming and monte-carlo tree search: A case study of hearthstone. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pages 2351–2358.
Costa, L. M., Drachen, A., Souza, F. C. M., e Xexéo, G. (2023). Artificial intelligence in moba games: A multivocal literature mapping. IEEE Transactions on Games, pages 1–23.
Darwin, C. (2003). A Origem das Espécies. Editora Hermus, São Paulo.
Egan, T. e Picton, P. (1994). Behaviour of a simple genetic algorithm searching for bright and edge pixels in an image. In IEE Colloquium on Genetic Algorithms in Image Processing and Vision, pages 2/1–2/6.
F. C. M. Souza, M. Papadakis, Y. L. T. e Delamaro, M. E. (2016). Strong mutation-based test data generation using hill climbing,. Proceedings of the 9th International Workshop on Search-Based Software Testing, ser. SBST ’16. ACM.
Games, R. (2024). Legends of runeterra.
García-Sánchez, P., Tonda, A., Squillero, G., Mora, A., e Merelo, J. J. (2016). Evolutionary deckbuilding in hearthstone. In 2016 IEEE Conference on Computational Intelligence and Games (CIG), pages 1–8.
K. Z. Zamli, B. Y. A. e Kendall, G. (2016). A tabu search hyper-heuristic strategy for t-way test suite generation. Applied Soft Computing, vol. 44.
Kramer, O. (2017). Genetic algorithm essentials. Springer.
LaSalle, D. e Karypis, G. (2016). A parallel hill-climbing refinement algorithm for graph partitioning,. Proceedings of the 45th International Conference on Parallel Processing, ser.ICPP’16.
Laurence, F. (2019). Receita de games deve atingir us 187 bi no mundo em 2023.
M. Scirea, J. Togelius, P. W. E. e Risi, S. (2016). Metacompose: acompositional evolutionary music composer. Proceedings of the Evolutionary and Biologically Inspired Music, Sound, Art and Design, ser.EvoStar.
Mitsis, K., Kalafatis, E., Zarkogianni, K., Mourkousis, G., e Nikita, K. S. (2020). Procedural content generation based on a genetic algorithm in a serious game for obstructive sleep apnea. In 2020 IEEE Conference on Games (CoG), pages 694–697.
Ward, C. D. e Cowling, P. I. (2009). Monte carlo search applied to card selection in magic: The gathering. In 2009 IEEE Symposium on Computational Intelligence and Games, pages 9–16.
Weise, T. (2008). Global optimization algorithms – theory and application, 1st ed.
Yang, Y., Yeh, T., e Chiang, T. (2021). Deck building in collectible card games using genetic algorithms: A case study of legends of code and magic. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pages 01–07.
Publicado
30/09/2024
Como Citar
BALBINOT, Ricardo A.; COSTA, Lincoln M.; SOUZA, Alinne C. Corrêa; SOUZA, Francisco Carlos M..
Developing Competitive Strategies for Legends of Runeterra using Genetic Algorithm. In: SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 23. , 2024, Manaus/AM.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 513-531.
DOI: https://doi.org/10.5753/sbgames.2024.241320.
