摘要
近年来,大型多人在线角色扮演游戏(MMORPG)已经成为最流行的网络娱乐活动之一。MMORPG在游戏环境中形成虚拟社会,其中每个玩家扮演某个虚构角色,并控制该角色的大多数活动。游戏的迅猛发展累积了海量数据,其中包含游戏虚拟社会的语义和拓扑信息。研究者针对游戏数据开展了一系列研究工作,如玩家退出预测、游戏服务器整合等。游戏角色的下一地点预测对提升游戏体验、改善游戏设计和检测游戏机器人均有十分重要的意义。目前,该项预测任务主要使用统计分析完成。然而,由于游戏数据具有海量特征,因此需要一种自动化的计算方法。文中提出了基于隐马尔科夫模型的游戏角色下一地点预测模型,该模型能够考虑与位置特性相关的不可观测的属性,同时兼顾游戏角色前期行为的影响。实验结果表明,与现有方法相比,该方法具有建模直观的特点,在稠密分布的MMORPG数据中能够得到更准确的下一地点预测结果。
In recent years,massively multiplayer online role-playing games(MMORPG)has become one of the most popular Internet recreational activities.MMORPG creates virtual societies,in which each user plays a fictional character,and controls most of its activities.With rapid development of MMORPG,it has accumulated massive data,which contain semantic as well as topological information of virtual societies.Researchers have already carried out many stu-dies,such as player departure prediction and server consolidation.The task of next place prediction is crucial to enhance gaming experience,improve game design and game bot detection,and most of next place prediction methods are based on statistical analysis.However,it is difficult to apply these methods in practice due to the characteristic of large scale of game data,and an automatic computation method to be developed.This paper proposed a next place prediction algorithm based on hidden Markov model(HMM).The model considers location characteristics as unobservable parameters,and takes the effects of previous actions of each game character into consideration.Experimental results with real MMORPG dataset show that our approach is intuitive and has better performance in dense distributed data than other existing methods for the task of next place prediction of MMORPG.
作者
佟振明
刘志鹏
TONG Zhen-ming;LIU Zhi-peng(College of Computer Science and Engineering,Sanjiang University,Nanjing 210012,China;School of Modern Posts and Institute of Modern Posts,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
出处
《计算机科学》
CSCD
北大核心
2018年第B11期453-457,共5页
Computer Science
基金
南京邮电大学校级科研基金(NY214126)资助
