基于进化算法的个性化程序图生成集成方法

W. Raffe, Fabio Zambetta, Xiaodong Li, Kenneth O. Stanley
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引用次数: 15

摘要

在本文中,我们提出了集成多个进化过程的个性化程序内容生成(PCG)策略。在这种情况下,我们提供了一个具体的解决方案,即在自上而下的动作射击游戏中个性化游戏地图,以适应个人玩家的喜好。随着玩家市场的多样化,对个性化PCG的需求正在稳步增长,这使得设计一款能够适应广泛偏好和技能的游戏变得更加困难。在这里呈现的解决方案中,地图的几何形状和几何形状中的内容密度在不同的进化过程中呈现和生成,玩家的偏好通过互动进化和作为推荐系统的玩家模型的组合被捕获和利用。所有这些组件都被执行到一个测试平台游戏中,并通过一个无监督的公共实验进行实验。这个解决方案是根据一个可信的随机基线来检验的,这个基线与独立游戏开发者所执行的随机地图生成器相当。结果表明,整个系统获得了更好的评级,几何和内容进化过程正在探索更多的解决方案空间,玩家偏好模型的平均预测精度与现有推荐系统文献相当。此外,我们还将讨论如何将每个解决方案用于其他游戏类型和内容类型。
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Integrated Approach to Personalized Procedural Map Generation Using Evolutionary Algorithms
In this paper, we propose the strategy of integrating multiple evolutionary processes for personalized procedural content generation (PCG). In this vein, we provide a concrete solution that personalizes game maps in a top-down action-shooter game to suit an individual player's preferences. The need for personalized PCG is steadily growing as the player market diversifies, making it more difficult to design a game that will accommodate a broad range of preferences and skills. In the solution presented here, the geometry of the map and the density of content within that geometry are represented and generated in distinct evolutionary processes, with the player's preferences being captured and utilized through a combination of interactive evolution and a player model formulated as a recommender system. All these components were implemented into a test bed game and experimented on through an unsupervised public experiment. The solution is examined against a plausible random baseline that is comparable to random map generators that have been implemented by independent game developers. Results indicate that the system as a whole is receiving better ratings, that the geometry and content evolutionary processes are exploring more of the solution space, and that the mean prediction accuracy of the player preference models is equivalent to that of existing recommender system literature. Furthermore, we discuss how each of the individual solutions can be used with other game genres and content types.
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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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