Towards Game Design via Creative Machine Learning (GDCML)
This work targets game designers by introducing a novel approach to content creation, but it is incremental as it builds on existing creative ML methods.
The paper addresses the underutilization of creative machine learning techniques in game design by proposing to adapt existing methods for generating game content, illustrated through example applications and a proposed system.
In recent years, machine learning (ML) systems have been increasingly applied for performing creative tasks. Such creative ML approaches have seen wide use in the domains of visual art and music for applications such as image and music generation and style transfer. However, similar creative ML techniques have not been as widely adopted in the domain of game design despite the emergence of ML-based methods for generating game content. In this paper, we argue for leveraging and repurposing such creative techniques for designing content for games, referring to these as approaches for Game Design via Creative ML (GDCML). We highlight existing systems that enable GDCML and illustrate how creative ML can inform new systems via example applications and a proposed system.