Abstract
Multiplayer Online Battle Arena (MOBA) is currently one of the most popular genres of digital games around the world. The domain of knowledge contained in these complicated games is large. It is hard for humans and algorithms to evaluate the real-time game situation or predict the game result. In this paper, we introduce MOBA-Slice, a time slice based evaluation framework of relative advantage between teams in MOBA games. MOBA-Slice is a quantitative evaluation method based on learning, similar to the value network of AlphaGo. It establishes a foundation for further MOBA related research including AI development. In MOBA-Slice, with an analysis of the deciding factors of MOBA game results, we design a neural network model to fit our discounted evaluation function. Then we apply MOBA-Slice to Defense of the Ancients 2 (DotA2), a typical and popular MOBA game. Experiments on a large number of match replays show that our model works well on arbitrary matches. MOBA-Slice not only has an accuracy 3.7% higher than DotA Plus Assistant (A subscription service provided by DotA2) at result prediction, but also supports the prediction of the remaining time of a game, and then realizes the evaluation of relative advantage between teams.
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Notes
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- 3.
Replay parser from OpenDota project: https://github.com/odota/parser.
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Clarity 2: https://github.com/skadistats/clarity.
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Documentation of Steam APIs for DotA2: https://wiki.teamfortress.com/wiki/WebAPI#Dota_2.
- 7.
Data processing took place in Oct. 2017.
- 8.
Documentation of OpenDota API for match data: https://docs.opendota.com/#tag/matches.
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Homepage of DotA Plus: https://www.dota2.com/plus.
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Homepage of DotA2 Asian Championship: http://www.dota2.com.cn/dac/2018/index/?l=english.
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Yu, L., Zhang, D., Chen, X., Xie, X. (2019). MOBA-Slice: A Time Slice Based Evaluation Framework of Relative Advantage Between Teams in MOBA Games. In: Cazenave, T., Saffidine, A., Sturtevant, N. (eds) Computer Games. CGW 2018. Communications in Computer and Information Science, vol 1017. Springer, Cham. https://doi.org/10.1007/978-3-030-24337-1_2
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