基于序列到序列结构的MOBA游戏局势趋势预测模型

多人在线战术竞技(MOBA)游戏是当前世界最流行的电子游戏类型之一,该类游戏涉及的知识领域相当复杂.随着电子竞技产业的飞速发展,数据分析对MOBA游戏的影响也越来越大,在对该类游戏的实时局势进行评价时,一般是选择过程变量作为指标,例如经济差、经验差,但目前缺少趋势预测的相关研究.针对该问题,提出一种基于序列到序列结构的MOBA游戏趋势预测模型(MOBA-Trend).在预处理阶段,针对该类游戏数据的特点,设计一种数据缩放算法体现数据间的重要度,并使用低通滤波器消除数据噪声;之后将双方阵容与历史战斗信息作为输入特征,构建带有注意力机制的序列模型,同时预测经济差、经验差;最后将模型应用于Dota 2,构建并发布相关数据集.实验结果表明,所提出的模型能够有效地预测序列的变化趋势.

MOBA game trend prediction model based on sequence-to-sequence structure

Multiplayer online battle arena(MOBA) is currently one of the most popular genres of digital games around the world. With the development of E-sports, the impact of data analysis on MOBA games is increasing. The in-game variables like gold & experience are generally selected as indicators to evaluate the real-time game situations. However, there are few previous studies on forecasting game-evolving trends. To learn the trend information in time-series data, we propose a MOBA game trend prediction model based on the sequence-to-sequence structure, called MOBA-Trend. Firstly, we design a data scaling algorithm and use a low-pass filter to eliminate noise in the data. Then, the model takes both lineups and historical variable sequences as inputs. And the seq2seq structure with attention mechanism is used to forecast the future trends of gold & experience. Finally, we apply the model to Dota2, one of the most popular MOBA games. Experiments on a large number of match replays show that the model can effectively forecast the evolving trends.