  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.
