Analysis of Logistic Map for Pseudorandom Number Generation in Game Development

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.