  Machine learning has been a popular tool in many different fields, including
procedural content generation. However, procedural content generation via
machine learning (PCGML) approaches can struggle with controllability and
coherence. In this paper, we attempt to address these problems by learning to
generate human-like paths, and then generating levels based on these paths. We
extract player path data from gameplay video, train an LSTM to generate new
paths based on this data, and then generate game levels based on this path
data. We demonstrate that our approach leads to more coherent levels for the
game Lode Runner in comparison to an existing PCGML approach.
