使用连续观察来模拟和预测玩家行为

Brent E. Harrison, D. Roberts
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引用次数: 52

摘要

在本文中,我们提出了一种用于设计用户行为模型的数据驱动技术。以前,玩家模型是通过用户调查、小规模观察实验或知识工程来设计的。这些方法通常产生语义上有意义的模型,但其适用性有限。为了解决这个问题,我们开发了一种纯粹的数据驱动方法,基于过去对其他玩家的观察来生成玩家模型。我们的基本假设是,如果我们从处于类似情况下的前玩家那里获得足够的数据,我们就可以准确地预测玩家在特定情况下会做什么。我们选择用MMORPG《魔兽世界》的成就数据来测试我们的方法。实验表明,我们的方法在准确率和召回率方面都大大优于基线算法,证明该方法可以仅基于观察数据创建准确的玩家模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using sequential observations to model and predict player behavior
In this paper, we present a data-driven technique for designing models of user behavior. Previously, player models were designed using user surveys, small-scale observation experiments, or knowledge engineering. These methods generally produced semantically meaningful models that were limited in their applicability. To address this, we have developed a purely data-driven methodology for generating player models based on past observations of other players. Our underlying assumption is that we can accurately predict what a player will do in a given situation if we examine enough data from former players that were in similar situations. We have chosen to test our method on achievement data from the MMORPG World of Warcraft. Experiments show that our method greatly outperforms a baseline algorithm in both precision and recall, proving that this method can create accurate player models based solely on observation data.
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