论文标题
使用机械频率和游戏状态痕迹预测角色
Predicting Personas Using Mechanic Frequencies and Game State Traces
论文作者
论文摘要
我们研究了如何根据PlayTraces有效预测游戏角色。可以通过计算玩家与游戏行为的生成模型之间的动作协议比率，即所谓的程序角色来计算。但这在计算上很昂贵，并假设很容易获得适当的程序性格。我们提出了两种用于估计玩家角色的方法，一种使用定期监督的学习和汇总游戏机制的量度，另一种是基于序列学习的序列学习的另一种方法。尽管这两种方法在预测与程序角色一致定义的游戏角色时都具有很高的精度，但它们完全无法预测玩家使用问卷的玩家本身定义的游戏风格。这个有趣的结果突出了使用计算方法定义游戏角色的价值。
We investigate how to efficiently predict play personas based on playtraces. Play personas can be computed by calculating the action agreement ratio between a player and a generative model of playing behavior, a so-called procedural persona. But this is computationally expensive and assumes that appropriate procedural personas are readily available. We present two methods for estimating player persona, one using regular supervised learning and aggregate measures of game mechanics initiated, and another based on sequence learning on a trace of closely cropped gameplay observations. While both of these methods achieve high accuracy when predicting play personas defined by agreement with procedural personas, they utterly fail to predict play style as defined by the players themselves using a questionnaire. This interesting result highlights the value of using computational methods in defining play personas.
