Towards Game Design via Creative Machine Learning (GDCML)

TL;DR

This paper advocates for applying creative machine learning techniques to game design, highlighting existing systems and proposing new approaches to generate game content through ML-driven methods.

Contribution

It introduces the concept of Game Design via Creative ML (GDCML), reviewing current systems and proposing a framework for integrating creative ML into game content creation.

Findings

Existing creative ML techniques can be adapted for game design.

Several systems already enable GDCML applications.

Proposed system illustrates potential for ML-driven game content generation.

Abstract

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.