Recent years, there has been growing interests in experience-driven
procedural level generation. Various metrics have been formulated to model
player experience and help generate personalised levels. In this work, we
question whether experience metrics can adapt to agents with different
personas. We start by reviewing existing metrics for evaluating game levels.
Then, focusing on platformer games, we design a framework integrating various
agents and evaluation metrics. Experimental studies on \emph{Super Mario Bros.}
indicate that using the same evaluation metrics but agents with different
personas can generate levels for particular persona. It implies that, for
simple games, using a game-playing agent of specific player archetype as a
level tester is probably all we need to generate levels of diverse behaviour
engagement.

探讨了如何使用具有不同个性的代理来生成具有个性化的等级， 通过 Super Mario Bros 的实验研究，发现使用相同的评估指标，但不同个性的代理可以生成特定个性的等级。

生成多样化行为参与的游戏关卡

Generating Game Levels of Diverse Behaviour Engagement

Procedural Level Generation via Machine Learning (PLGML), the study of
generating game levels with machine learning, has received a large amount of
recent academic attention. For certain measures these approaches have shown
success at replicating the quality of existing game levels. However, it is
unclear the extent to which they might benefit human designers. In this paper
we present a framework for co-creative level design with a PLGML agent. In
support of this framework we present results from a user study and results from
a comparative study of PLGML approaches.

本文介绍了一个通过 PLGML 代理进行共创级别设计的框架，并通过用户研究和比较研究的结果来支持该框架。

机器学习辅助下的协同创作级别设计

Co-Creative Level Design via Machine Learning

Deep reinforcement learning (RL) has shown impressive results in a variety of
domains, learning directly from high-dimensional sensory streams. However, when
neural networks are trained in a fixed environment, such as a single level in a
video game, they will usually overfit and fail to generalize to new levels.
When RL models overfit, even slight modifications to the environment can result
in poor agent performance. This paper explores how procedurally generated
levels during training can increase generality. We show that for some games
procedural level generation enables generalization to new levels within the
same distribution. Additionally, it is possible to achieve better performance
with less data by manipulating the difficulty of the levels in response to the
performance of the agent. The generality of the learned behaviors is also
evaluated on a set of human-designed levels. The results suggest that the
ability to generalize to human-designed levels highly depends on the design of
the level generators. We apply dimensionality reduction and clustering
techniques to visualize the generators' distributions of levels and analyze to
what degree they can produce levels similar to those designed by a human.

本文探讨了通过在训练中使用过程化生成的关卡如何增加模型的泛化性能，并研究了其与人类设计的关卡的关系。结果表明，通过降低难度、调整关卡设计，可以获得更好的性能表现，并进行了降维和聚类分析来评估关卡生成器的分布。