Using artificial intelligence (AI) to automatically test a game remains a
critical challenge for the development of richer and more complex game worlds
and for the advancement of AI at large. One of the most promising methods for
achieving that long-standing goal is the use of generative AI agents, namely
procedural personas, that attempt to imitate particular playing behaviors which
are represented as rules, rewards, or human demonstrations. All research
efforts for building those generative agents, however, have focused solely on
playing behavior which is arguably a narrow perspective of what a player
actually does in a game. Motivated by this gap in the existing state of the
art, in this paper we extend the notion of behavioral procedural personas to
cater for player experience, thus examining generative agents that can both
behave and experience their game as humans would. For that purpose, we employ
the Go-Explore reinforcement learning paradigm for training human-like
procedural personas, and we test our method on behavior and experience
demonstrations of more than 100 players of a racing game. Our findings suggest
that the generated agents exhibit distinctive play styles and experience
responses of the human personas they were designed to imitate. Importantly, it
also appears that experience, which is tied to playing behavior, can be a
highly informative driver for better behavioral exploration.

本研究考虑将行为规则、奖励和人类示范化为生成 AI 代理即程序化人物角色，以模拟人类玩家的游戏体验，通过 Go-Explore 强化学习方法来训练人类化的代理，其结果显示生成的代理呈现出人类玩家的不同游戏风格和体验响应，同时对玩家体验的考虑，能够更好地推动行为探索。

表现和体验像人类的生成角色

Generative Personas That Behave and Experience Like Humans

The process of playtesting a game is subjective, expensive and incomplete. In
this paper, we present a playtesting approach that explores the game space with
automated agents and collects data to answer questions posed by the designers.
Rather than have agents interacting with an actual game client, this approach
recreates the bare bone mechanics of the game as a separate system. Our agent
is able to play in minutes what would take testers days of organic gameplay.
The analysis of thousands of game simulations exposed imbalances in game
actions, identified inconsequential rewards and evaluated the effectiveness of
optional strategic choices. Our test case game, The Sims Mobile, was recently
released and the findings shown here influenced design changes that resulted in
improved player experience.

本文提出了一种使用自动代理进行游戏测试的方法，该方法可以以分钟级的速度完成测试者需要数天才能完成的有机游戏。该测试方法的应用在《模拟人生移动版》中取得了积极的效果，对游戏的平衡性，奖励设置以及选项效果进行了评估和优化，从而提升了玩家体验。