Abstract
The existing research on MOBA game lineup recommendation mainly takes the heroes with high winning rate in the historical game as the recommendation results, without comprehensively considering the synergy relationship and the counter relationship between heroes. In the recommended results, there will be a situation where a hero has great ability, but the synergy between the heroes is not good, or the recommended hero is counter by the enemy. This paper summarizes the problem as a multi-objective optimization problem, and proposes a MOBA game lineup recommendation method base on NSGA-II. Firstly, based on the performance of the heroes in historical games, the ability of these heroes is quantified. At the same time, according to different heroes as teammates or opponents, the synergy relationship and the counter relationship between heroes are evaluated. Then, NSGA-II is used to solve the multi-objective optimization problem, and the recommendation result is obtained. Finally, the results are evaluated based on prediction models. The method proposed in this paper integrates the hero’s ability, the synergy relationship and the counter relationship between heroes, which has strong rationality. Experimental results show that the method has high accuracy, and can provide a new idea for solving this kind of problems.
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References
Song, K., Zhang, T., Ma, C.: Predicting the winning side of DotA2. In: Sl: sn. (2015)
Chen, Z., Xu, Y., et al.: The art of drafting: a team-oriented hero recommendation system for multiplayer online battle arena games. In: ACM Conference, pp. 200–208 (2018)
Costa, L.M., Souza, A.C.C., Souza, F.C.M.: An approach for team composition in league of legends using genetic algorithm. In: IEEE, pp. 52–61 (2019)
Wang, L., Zeng, Y., Chen, B., Pan, Y., Cao, L.: Team recommendation using order-based fuzzy integral and NSGA-II in starcraft. In: IEEE Access, pp. 59559–59570 (2020)
Gourdeau, D., Archambault, L.: Discriminative neural network for hero selection in professional heroes of the storm and DOTA 2. In: IEEE Transactions on Games (2020)
Agarwala, A., Pearce, M.: Learning Dota 2 team compositions. In: Sl: sn. (2014)
Conley, K., Perry, D.: How does he saw me? a recommendation engine for picking heroes in dota 2. In: Np, nd Web, p. 7 (2013)
Wang, N., Li, L., Xiao, L., Yang, Zhou, Y.: Outcome prediction of dota2 using machine learning methods. In: Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence, pp. 61–67 (2018). https://doi.org/10.1145/3208788.3208800
Kinkade, N., Lim, K.: DOTA2 win prediction. In: Univ Calif, pp. 1–13 (2015)
Xu, C.: Research of Dota2 lineup recommendation and winning percentage prediction system based on deep learning. In: Henan University (2019)
He, M., Ma, X., Jin, Y.: Station importance evaluation in dynamic bike-sharing rebalancing optimization using an entropy-based TOPSIS approach. In: IEEE Access, pp. 38119–38131 (2021). https://doi.org/10.1109/ACCESS.2021.3063881
Lian, L., Ouyang, T., Ma, F., Liu, J.: Fuzzy integral sliding mode control based on microbial fuel cell. In: Complexity, pp. 1–8 (2021). https://doi.org/10.1155/2021/6670039
Yusuf, D., Rew, M., Davis, G., et al.: An alternative method for the evaluation of docking performance: rsr vs rmsd. J. Chem. Inf. Model. 48(7), 1411–1422 (2008). https://doi.org/10.1021/ci800084x
Dotamax Homepage. www.dotamax.com. Accessed 25 July 2021
Wang, H., Kou, Y., Shen, D., Nie, T.: An explainable recommendation method based on multi-timeslice graph embedding. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds.) WISA 2020. LNCS, vol. 12432, pp. 84–95. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60029-7_8
Acknowledgment
This work is supported by the National Natural Science Foundation of China (62072084, 62072086), the National Defense Basic Scientific Research Pr-ogram of China (JCKY2018205C012) and the Fundamental Research Funds for the C-entral Universities (N2116008).
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Li, M. et al. (2021). A Method of MOBA Game Lineup Recommendation Based on NSGA-II. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_49
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DOI: https://doi.org/10.1007/978-3-030-87571-8_49
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