Abstract
Multiplayer Online Battle Arena (MOBA) game is currently one of the most popular genres of online games. In a MOBA game, players in a team compete against an opposing team. Typically, each MOBA game is a larger battle composed of a series of combat events. During a combat, the behavior of each player varies and the outcome of a game is determined both by the variation of each player’s behavior and by the interactions within each instance of combat. However, both the variation and interaction are highly dynamic and difficult to master, making it hard to predict the outcome of a game. In this paper, we present a player behavior model (called pb-model). The model allows us to predict the result of a game once we have collected enough data on the behaviour of the players. We first use convolution to extract the features of player behavior variation in each combat and model them as sequences by time. Then we use a recurrent neural network to process the interaction among these sequences. Finally, we combine these two structures in a network to predict the result of a game. Experiments performed on typical MOBA game dataset verify that our pb-model is effective and achieves as high as 87.85% prediction accuracy.
This work was supported in part by the National Natural Science Foundation of China under grant No. 1572332, the Fundamental Research Funds for the Central Universities under grant No. 2016SCU04A22, and the China Postdoctoral Science Foundation under grant No. 2016T90850.
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Lan, X., Duan, L., Chen, W., Qin, R., Nummenmaa, T., Nummenmaa, J. (2018). A Player Behavior Model for Predicting Win-Loss Outcome in MOBA Games. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_41
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