A multi-size convolution neural network for RTS games winner prediction

摘要：

　　Researches of AI planning in Real-Time Strategy(RTS)games have been widely applied to human behavior modeling and combat simulation.Winner prediction is an important research area for AI planning,which ensures the decision accuracy.In this paper,we introduce an effective architecture--multi-size convolution neural network(MSCNN)--into winner prediction.It can capture more feature for game states,because of the various sizes of filters in MSCNN.Experiments show that the modified evaluating algorithm can effectively improve the accuracy of winner prediction for RTS games.

作者: Jie Huang Weilong Yang

作者单位: College of System Engineering,National University of Defense Technology,Changsha,China

会议类型: 国际会议

会议名称: 2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)(2018第二届电子信息技术与计算机工程国际会议)(EITCE2018)

会议地点: 上海

会议语种:英文

页码: 1-5

在线出版日期: 2018-10-12（万方平台首次上网日期，不代表论文的发表时间）