Temporal Game Challenge Tailoring

Alexander Zook, Mark O. Riedl
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引用次数: 9

Abstract

Digital games often center on a series of challenges designed to vary in difficulty over the course of the game. Designers, however, lack ways to ensure challenges are suitably tailored to the abilities of each game player, often resulting in player boredom or frustration. Challenge tailoring refers to the general problem of matching designer-intended challenges to player abilities. We present an approach to predict temporal player performance and select appropriate content to solve the challenge tailoring problem. Our temporal collaborative filtering approach-tensor factorization-captures similarities among players and the challenges they face to predict player performance on unseen, future challenges. Tensor factorization accounts for varying player abilities over time and is a generic approach capable of modeling many kinds of players. We use constraint solving to optimize content selection to match player skills to a designer-specified level of performance and present a model-performance curves-for designers to specify desired, temporally changing player behavior. We evaluate our approach in a role-playing game through two empirical studies of humans and one study using simulated agents. Our studies show tensor factorization scales in multiple game-relevant data dimensions, can be used for modestly effective game adaptation, and can predict divergent player learning trends.
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时间游戏挑战裁剪
数字游戏通常以一系列挑战为中心,在游戏过程中设计不同难度的挑战。然而,设计师缺乏确保挑战适合每个玩家能力的方法,这通常会导致玩家感到无聊或受挫。挑战裁剪指的是将设计师设计的挑战与玩家能力相匹配的问题。我们提出了一种方法来预测玩家的时间表现,并选择合适的内容来解决挑战裁剪问题。我们的时间协同过滤方法——张量分解——捕捉玩家之间的相似性和他们面临的挑战,以预测玩家在未知的未来挑战中的表现。张量分解解释了玩家能力随时间的变化,是一种能够模拟多种玩家的通用方法。我们使用约束求解来优化内容选择,将玩家技能与设计师指定的性能水平相匹配,并呈现一个模型-性能曲线-供设计师指定所需的,暂时改变的玩家行为。我们通过两项人类的实证研究和一项使用模拟代理的研究来评估我们在角色扮演游戏中的方法。我们的研究表明,在多个游戏相关的数据维度中,张量分解尺度可以用于适度有效的游戏适应,并且可以预测不同的玩家学习趋势。
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来源期刊
IEEE Transactions on Computational Intelligence and AI in Games
IEEE Transactions on Computational Intelligence and AI in Games COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.60
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation. The IEEE Transactions on Computational Intelligence and AI in Games (T-CIAIG) publishes archival journal quality original papers in computational intelligence and related areas in artificial intelligence applied to games, including but not limited to videogames, mathematical games, human–computer interactions in games, and games involving physical objects. Emphasis is placed on the use of these methods to improve performance in and understanding of the dynamics of games, as well as gaining insight into the properties of the methods as applied to games. It also includes using games as a platform for building intelligent embedded agents for the real world. Papers connecting games to all areas of computational intelligence and traditional AI are considered.
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