基于序列到序列结构的MOBA游戏局势趋势预测模型
作者:
作者单位:

1.东北大学;2.北京机电工程总体设计部

作者简介:

通讯作者:

中图分类号:

TP391

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


MOBA Game Trend Prediction Model Based on Sequence-to-Sequence Structure
Author:
Affiliation:

1.Northeastern University;2.Beijing system design institute of the Electro-mechanic Engineering

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    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,在该类游戏中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。本文提出了MOBA-Trend,一种基于序列到序列结构的MOBA游戏趋势预测模型。在预处理阶段,针对该类游戏数据的特点,设计了一个数据缩放算法来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Dota2,构建并发布了相关数据集,实验结果表明,本文提出的模型能够有效地预测序列的变化趋势。

    Abstract:

    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 MOBA-Trend, a MOBA game trend prediction model based on the sequence-to-sequence structure. First 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 input. And the seq2seq structure with attention mechanism is used to forecast the future trends of gold \& experience. Finally, we applied 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.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-05-23
  • 最后修改日期:2022-01-21
  • 录用日期:2022-01-28
  • 在线发布日期: 2022-03-01
  • 出版日期: