Many popular video games use pseudorandom number generators to create randomly distributed locations for game objects as highly unpredictable as possible. Some scenarios like game competition also need reproducible randomness, namely the random results can be reproducible if given the same seed input. Existing random generation methods have limited choices for seed input. To address this limitation, this study analyzes a chaotic map called the Logistic Map for game development. After analyzing the properties of this chaotic map, I developed a pseudorandom sequence generation algorithm and a generation algorithm of random locations of game objects. Experiments on the game of Snake demonstrate that the Logistic Map is viable for game development. The reproducible randomness is also realized with the proposed algorithm.

翻译：许多热门电子游戏使用伪随机数生成器来创建尽可能不可预测的游戏对象随机分布位置。诸如游戏竞技等场景还需要可复现的随机性，即给定相同种子输入时，随机结果可被重复生成。现有随机生成方法的种子输入选择有限。为解决这一局限，本研究分析了一种名为Logistic映射的混沌映射在游戏开发中的应用。通过分析该混沌映射的性质，我们开发了伪随机序列生成算法及游戏对象随机位置生成算法。在贪吃蛇游戏上的实验表明，Logistic映射可用于游戏开发，且所提算法实现了可复现的随机性。