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Search-based procedural content generation methods have recently beenintroduced for the autonomous creation of bullet hell games. Search-basedmethods, however, can hardly model patterns of danmakus -- the bullet hellshooting entity -- explicitly and the resulting levels often looknon-realistic. In this paper, we present a novel bullet hell game platformnamed Keiki, which allows the representation of danmakus as a parametricsequence which, in turn, can model the sequential behaviours of danmakus. Weemploy three types of generative adversarial networks (GANs) and test Keikiacross three metrics designed to quantify the quality of the generateddanmakus. The time-series GAN and periodic spatial GAN show different yetcompetitive performance in terms of the evaluation metrics adopted, theirdeviation from human-designed danmakus, and the diversity of generateddanmakus. The preliminary experimental studies presented here showcase thatpotential of time-series GANs for sequential content generation in games.

Keiki: Towards Realistic Danmaku Generation via Sequential GANs