  Search-based procedural content generation methods have recently been
introduced for the autonomous creation of bullet hell games. Search-based
methods, however, can hardly model patterns of danmakus -- the bullet hell
shooting entity -- explicitly and the resulting levels often look
non-realistic. In this paper, we present a novel bullet hell game platform
named Keiki, which allows the representation of danmakus as a parametric
sequence which, in turn, can model the sequential behaviours of danmakus. We
employ three types of generative adversarial networks (GANs) and test Keiki
across three metrics designed to quantify the quality of the generated
danmakus. The time-series GAN and periodic spatial GAN show different yet
competitive performance in terms of the evaluation metrics adopted, their
deviation from human-designed danmakus, and the diversity of generated
danmakus. The preliminary experimental studies presented here showcase that
potential of time-series GANs for sequential content generation in games.
