动态游戏适应提高休闲游戏会话留存率的分析与心理测量学评价

Brent E. Harrison, D. Roberts
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引用次数: 6

摘要

本文展示了如何使用游戏分析来动态调整休闲游戏环境,从而提高会话级留存率。我们的技术包括使用游戏分析来创建一个抽象的游戏分析空间,使问题易于处理。然后,我们在这一领域对玩家留存率进行建模,并使用这些模型对游戏分析进行指导性改变,从而带来有针对性的游戏状态分布,从而影响玩家行为。实验表明,两款不同的休闲游戏《Scrabblesque》和《Sidequest: the Game》的自适应版本能够更好地适应游戏状态的目标分布,同时与非自适应版本的游戏相比,也显著降低了玩家的退出率。我们通过测量玩家内在动机和对游戏环境的沉浸度的心理评估,证明了这些收益并不是以牺牲玩家体验为代价的。在这两种情况下,我们发现玩自适应版本游戏的玩家比玩非自适应版本游戏的玩家报告了更高的内在动机和粘性分数。
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An Analytic and Psychometric Evaluation of Dynamic Game Adaption for Increasing Session-Level Retention in Casual Games
This paper shows how game analytics can be used to dynamically adapt casual game environments in order to increase session-level retention. Our technique involves using game analytics to create an abstracted game analytic space to make the problem tractable. We then model player retention in this space and use these models to make guided changes to game analytics in order to bring about a targeted distribution of game states that will, in turn, influence player behavior. Experiments performed showed that the adaptive versions of two different casual games, Scrabblesque and Sidequest: The Game, were able to better fit a target distribution of game states while also significantly reducing the quitting rate compared to the nonadaptive version of the games. We showed that these gains were not coming at the cost of player experience by performing a psychometric evaluation in which we measured player intrinsic motivation and engagement with the game environments. In both cases, we showed that players playing the adaptive version of the games reported higher intrinsic motivation and engagement scores than players playing the nonadaptive version of the games.
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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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