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

IF 2.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Davor Hafnar;Jure Demšar
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引用次数: 0

Abstract

Procedural content generation uses algorithmic techniques to create large amounts of new content for games at much lower production costs. To improve its quality, 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 to player preferences benefits both players, by enhancing enjoyment, and developers, who rely on player satisfaction for monetization. Therefore, this article introduces a new method for personalization by using large language models to suggest levels based on ongoing gameplay data from each player. 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 two aspects—the player's rating of levels and the probability that a player will not quit the game mid-level.
零点推理:无冷启动问题的个性化内容生成
程序内容生成使用算法技术以更低的制作成本为游戏创造大量新内容。为了提高质量,在更新的方法中,程序内容生成利用了机器学习。然而,这些方法通常需要昂贵的大量数据收集,以及相当复杂的学习模型的开发和训练,这既耗时又昂贵。我们研究的核心是探索我们是否可以通过更实用和可推广的方法与大型语言模型来降低使用个性化程序内容生成的障碍。将游戏内容与玩家偏好相匹配,既能提高玩家的乐趣,也能让依靠玩家满意度盈利的开发者受益。因此,本文介绍了一种新的个性化方法,即使用大型语言模型根据每个玩家的持续玩法数据来建议关卡。我们将使用我们的方法生成的关卡与使用传统程序生成技术生成的关卡进行了比较。我们易于复制的方法在生产环境中被证明是可行的,并且在两个方面优于传统方法生成的关卡——玩家对关卡的评价和玩家不会中途退出游戏的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Games
IEEE Transactions on Games Engineering-Electrical and Electronic Engineering
CiteScore
4.60
自引率
8.70%
发文量
87
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