多人在线游戏中的玩家行为与最优团队组成
Player Behavior and Optimal Team Composition for Online Multiplayer Games
摘要：
我们考虑聚类玩家行为和学习多人在线游戏的最佳团队组成。我们的目标是确定一组描述性的游戏风格分组，并学习赢/输结果的预测。预测器将每个团队中的参与者的游戏风格作为输入;即，一场比赛中不同球队的组合。我们的框架使用无监督学习来找到行为集群，这些行为集群反过来与分类算法一起使用来学习结果预测器。对于我们的数值实验，我们考虑英雄联盟，一个流行的团队为基础的角色扮演游戏开发的Riot Games。我们观察到的学习集群不仅证实了游戏知识，而且还提供了令专家玩家惊讶的见解。我们还表明，游戏的结果可以预测相当高的准确性给定的团队组合为基础的功能。
英文摘要：
We consider clustering player behavior and learning the optimal team composition for multiplayer online games. The goal is to determine a set of descriptive play style groupings and learn a predictor for win/loss outcomes. The predictor takes in as input the play styles of the participants in each team; i.e., the various team compositions in a game. Our framework uses unsupervised learning to find behavior clusters, which are, in turn, used with classification algorithms to learn the outcome predictor. For our numerical experiments, we consider League of Legends, a popular team-based role-playing game developed by Riot Games. We observe the learned clusters to not only corroborate well with game knowledge, but also provide insights surprising to expert players. We also demonstrate that game outcomes can be predicted with fairly high accuracy given team composition-based features.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
