论文标题
使用来自异质传感器的数据对电子竞技播放器性能的预测预测
AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors
论文作者
论文摘要
电子竞技的新兴进步缺乏确保Pro和业余电子竞技团队中高质量分析和培训的工具。我们报告了启用人工智能（AI）的解决方案，用于使用传感器中的数据预测电子竞技播放器在游戏中的性能。因此，我们从专业人士和业余玩家那里收集了生理，环境和游戏主持人数据。使用经常性的神经网络，每时刻都会从多人游戏中的游戏日志中评估玩家的性能。我们已经调查了注意机制改善了网络的概括，并提供了直接的特征。最佳模型可以达到ROC AUC得分0.73。尽管未在培训集中使用他的数据，但实现了特定玩家表现的预测。拟议的解决方案为Pro Esports团队和业余参与者（例如学习工具或性能监视系统）提供了许多有希望的应用程序。
The emerging progress of eSports lacks the tools for ensuring high-quality analytics and training in Pro and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected the physiological, environmental, and the game chair data from Pro and amateur players. The player performance is assessed from the game logs in a multiplayer game for each moment of time using a recurrent neural network. We have investigated that attention mechanism improves the generalization of the network and provides the straightforward feature importance as well. The best model achieves ROC AUC score 0.73. The prediction of the performance of particular player is realized although his data are not utilized in the training set. The proposed solution has a number of promising applications for Pro eSports teams and amateur players, such as a learning tool or a performance monitoring system.
