  Video game level generation based on machine learning (ML), in particular,
deep generative models, has attracted attention as a technique to automate
level generation. However, applications of existing ML-based level generations
are mostly limited to tile-based level representation. When ML techniques are
applied to game domains with non-tile-based level representation, such as Angry
Birds, where objects in a level are specified by real-valued parameters, ML
often fails to generate playable levels. In this study, we develop a
deep-generative-model-based level generation for the game domain of Angry
Birds. To overcome these drawbacks, we propose a sequential encoding of a level
and process it as text data, whereas existing approaches employ a tile-based
encoding and process it as an image. Experiments show that the proposed level
generator drastically improves the stability and diversity of generated levels
compared with existing approaches. We apply latent variable evolution with the
proposed generator to control the feature of a generated level computed through
an AI agent's play, while keeping the level stable and natural.
