Estimation of player's preference for cooperative RPGs using multi-strategy Monte-Carlo method
Estimation of player's preference for cooperative RPGs using multi-strategy Monte-Carlo method
复制标题
使用多策略蒙特卡罗方法估计玩家对合作角色扮演游戏的偏好
DOI：
10.1109/cig.2015.7317935
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
T. Wada
中科院分区：
文献类型：
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作者：
Naoyuki Sato;Kokolo Ikeda;T. Wada
在许多电子游戏中，如角色扮演游戏（rpg）或体育游戏，电脑玩家不仅扮演人类玩家的对手，而且扮演队友的角色。但是作为队友的电脑玩家的行为往往出乎人类玩家的意料，而这种不匹配会比作为对手的电脑玩家引起更大的不满。造成这种不匹配的原因之一是，这些游戏中存在几种类型的子目标或游戏风格，而AI玩家的行为并不理解人类玩家对它们的偏好。本研究的目的是提出一种开发计算机队友玩家的方法，该方法可以估计队友人类玩家的子目标偏好并根据这些偏好采取行动。为此，我们将子目标的偏好建模为一个函数，并通过参考队友人类球员过去选择的动作，通过多策略蒙特卡罗方法确定最可能的参数。此外，我们还通过两个系列的实验对所提出的方法进行了评估，一个是使用具有不同子目标偏好的人工玩家，另一个是使用人类玩家。实验表明，该方法可以在几局游戏后估计出玩家的偏好，从而降低人类玩家的不满意度。
In many video games such as role playing games (RPGs) or sports games, computer players act not only as the opponents of the human player but also as team-mates. But computer players as team-mates often behave in a way that human players do not expect, and such mismatches cause bigger dissatisfaction than in the case of computer players as opponents., One of the reasons for such mismatches is that there are several types of sub-goals or play-styles in these games and the AI players act without understanding the human player's preference about them. The purpose of this study is to propose a method for developing computer team-mate players that estimate the sub-goal preferences of the team-mate human player and act according to these preferences., For this purpose, we modeled the preferences of sub-goals as a function and decided the most likely parameters by a multi-strategy Monte-Carlo method, by referring to the past actions selected by the team-mate human player., Additionally, we evaluated the proposed method through two series of experiments, one by using artificial players with various sub-goal preferences and another one by using human players. The experiments showed that the proposed method can estimate their preferences after a few games, and can decrease the dissatisfaction of human players.