Abstract
Honor of Kings is a multiplayer online battle arena game in which two teams fight with each other with five players controlling five different heroes on each side. By 2017, Honor of Kings has over 80 million daily active players and 200 million monthly active players and was both the world’s most popular and highest-grossing game of all time as well as the most downloaded gaming app globally. In this paper, we will introduce a prediction model based on a machine learning algorithm to forecast the victory of Honor of Kings 5V5 game by considering the heroes formation on each side using a gaming history dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Honor of Kings official website. https://pvp.qq.com/. Accessed 10 Oct 2019
Conley, K., Perry, D.: How does he saw me? A recommendation engine for picking heroes in DotA 2. Np, and Web 7 (2013)
Kalyanaraman, K: To win or not to win? A prediction model to determine the outcome of a DotA2 match. Technical report, University of California San Diego (2014)
Semenov, A., Romov, P., Korolev, S., Yashkov, D., Neklyudov, K.: Performance of machine learning algorithms in predicting game outcome from drafts in DotA 2. In: Ignatov, D., et al. (eds.) AIST 2016. CCIS, vol. 661, pp. 26–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-52920-2_3
Wang, K., Shang, W.: Outcome prediction of DOTA2 based on Naïve Bayes classifier. In: 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), pp. 591–593. IEEE (2017)
Song, K., Zhang, T., Ma, C.: Predicting the winning side of DotA2. Technical report, Stanford University (2015)
Drachen, A., Yancey, M., Klabajan, D., et al.: Skill-based differences in spatio-temporal team behaviour in defense of the Ancients 2 (DotA 2). In: 2014 IEEE Games Media Entertainment, pp. 1–8. IEEE (2014)
Semenov, A., et al. Applications of machine learning in DotA2: literature review and practical knowledge sharing. In: MLSA@ PKDD/ECML (2016)
Kinkade, N., Jolla, L., Lim, K.: DotA 2 win prediction. Technical report, University of California San Diego (2015)
Agarwala, A., Pearce, M.: Learning DotA 2 team compositions. Technical report, Stanford University (2014)
Acknowledgments
The work was supported by Key Technologies Research and Development Program of China (2017YFC0405805-04) and Basal Research Fund of China (2018B57614).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, L., Tang, Y., Liu, J. (2020). WPQA: A Gaming Support System Based on Machine Learning and Knowledge Graph. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Communications in Computer and Information Science, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-3412-6_19
Download citation
DOI: https://doi.org/10.1007/978-981-15-3412-6_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3411-9
Online ISBN: 978-981-15-3412-6
eBook Packages: Computer ScienceComputer Science (R0)