SuSketch: Surrogate Models of Gameplay as a Design Assistant

TL;DR

SuSketch is a novel AI-powered design tool that predicts gameplay outcomes and suggests level adjustments in first-person shooter games, aiding designers through an interactive, machine learning-based co-creation process.

Contribution

Introduces SuSketch, a surrogate modeling system that integrates machine learning into game level design, providing predictive feedback and suggestions for balancing gameplay.

Findings

User study showed SuSketch is easy to use.

Highlights need for better accessibility and explainability.

Demonstrates effective integration of AI in game design.

Abstract

This paper introduces SuSketch, a design tool for first person shooter levels. SuSketch provides the designer with gameplay predictions for two competing players of specific character classes. The interface allows the designer to work side-by-side with an artificially intelligent creator and to receive varied types of feedback such as path information, predicted balance between players in a complete playthrough, or a predicted heatmap of the locations of player deaths. The system also proactively designs alternatives to the level and class pairing, and presents them to the designer as suggestions that improve the predicted balance of the game. SuSketch offers a new way of integrating machine learning into mixed-initiative co-creation tools, as a surrogate of human play trained on a large corpus of artificial playtraces. A user study with 16 game developers indicated that the tool was easy to use, but also highlighted a need to make SuSketch more accessible and more explainable.