Procedural content generation uses algorithmic techniques to create large
amounts of new content for games at much lower production costs. In newer
approaches, procedural content generation utilizes machine learning. However,
these methods usually require expensive collection of large amounts of data, as
well as the development and training of fairly complex learning models, which
can be both extremely time-consuming and expensive. The core of our research is
to explore whether we can lower the barrier to the use of personalized
procedural content generation through a more practical and generalizable
approach with large language models. Matching game content with player
preferences benefits both players, who enjoy the game more, and developers, who
increasingly depend on players enjoying the game before being able to monetize
it. Therefore, this paper presents a novel approach to achieving
personalization by using large language models to propose levels based on the
gameplay data continuously collected from individual players. We compared the
levels generated using our approach with levels generated with more traditional
procedural generation techniques. Our easily reproducible method has proven
viable in a production setting and outperformed levels generated by traditional
methods in the probability that a player will not quit the game mid-level.

通过使用大型语言模型，本研究提出了一种新颖的方法来通过连续收集个体玩家的游戏数据，实现个性化的程式化内容生成，与传统方法相比，我们的方法在减少玩家在游戏关卡中中途退出的概率方面表现出更好的性能。

零样本推理：个性化内容生成无起步问题

Zero-Shot Reasoning: Personalized Content Generation Without the Cold  Start Problem

Is it possible to predict the motivation of players just by observing their
gameplay data? Even if so, how should we measure motivation in the first place?
To address the above questions, on the one end, we collect a large dataset of
gameplay data from players of the popular game Tom Clancy's The Division. On
the other end, we ask them to report their levels of competence, autonomy,
relatedness and presence using the Ubisoft Perceived Experience Questionnaire.
After processing the survey responses in an ordinal fashion we employ
preference learning methods based on support vector machines to infer the
mapping between gameplay and the reported four motivation factors. Our key
findings suggest that gameplay features are strong predictors of player
motivation as the best obtained models reach accuracies of near certainty, from
92% up to 94% on unseen players.

收集了大量玩家的 Tom Clancy's The Division 的游戏数据和 Ubisoft 感知体验问卷调查，并使用基于支持向量机的偏好学习方法来推断游戏特征和玩家动机之间的映射关系，发现游戏特征是预测玩家动机的强有力因素。

你的游戏表现说明一切：在汤姆・克兰西的全境封锁中建模动机

Your Gameplay Says It All: Modelling Motivation in Tom Clancy's The  Division

Machine playtesting tools and game moment search engines require exposure to
the diversity of a game's state space if they are to report on or index the
most interesting moments of possible play. Meanwhile, mobile app distribution
services would like to quickly determine if a freshly-uploaded game is fit to
be published. Having access to a semantic map of reachable states in the game
would enable efficient inference in these applications. However, human gameplay
data is expensive to acquire relative to the coverage of a game that it
provides. We show that off-the-shelf automatic exploration strategies can
explore with an effectiveness comparable to human gameplay on the same
timescale. We contribute generic methods for quantifying exploration quality as
a function of time and demonstrate our metric on several elementary techniques
and human players on a collection of commercial games sampled from multiple
game platforms (from Atari 2600 to Nintendo 64). Emphasizing the diversity of
states reached and the semantic map extracted, this work makes productive
contrast with the focus on finding a behavior policy or optimizing game score
used in most automatic game playing research.

研究表明，现成的自动勘探策略可以与人类游戏玩家在相同时间尺度上实现相同的效果，为探索游戏状态空间和提取语义地图等应用贡献了通用方法。