Explainable PCGML via Game Design Patterns

TL;DR

This paper introduces an explainable approach to procedural content generation using machine learning, leveraging game design patterns to improve user interaction and understanding, especially for non-expert designers.

Contribution

It proposes a novel explainability method for PCGML based on design patterns, enhancing user control and comprehension without requiring ML expertise.

Findings

Outperforms non-explainable systems in user interactions

Effective for designers without ML background

Improves understanding and manipulation of generated content

Abstract

Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise.