# SuSketch: Surrogate Models of Gameplay as a Design Assistant

arXiv: https://arxiv.org/abs/2103.11726 · HTML: https://mlsanity.com/paper/2103.11726/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Deep Learning for Procedural Content Generation](https://mlsanity.com/paper/2010.04548/) (2020)
- [Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creators](https://mlsanity.com/paper/1901.06417/) (2019)
- [10 Years of the PCG workshop: Past and Future Trends](https://mlsanity.com/paper/2104.11037/) (2021)
- [Demonstrating the Feasibility of Automatic Game Balancing](https://mlsanity.com/paper/1603.03795/) (2016)
- [Pitako -- Recommending Game Design Elements in Cicero](https://mlsanity.com/paper/1907.03877/) (2019)
- [Mek: Mechanics Prototyping Tool for 2D Tile-Based Turn-Based Deterministic Games](https://mlsanity.com/paper/1904.03540/) (2019)

## Built on by (most cited first, 6 of 6)

- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [Position: Interactive Generative Video as Next-Generation Game Engine](https://mlsanity.com/paper/2503.17359/) (2025)
- [TropeTwist: Trope-based Narrative Structure Generation](https://mlsanity.com/paper/2204.09672/) (2022)
- [Procedural Generation of 3D Maps with Snappable Meshes](https://mlsanity.com/paper/2108.00056/) (2021)
- [Evolutionary Machine Learning and Games](https://mlsanity.com/paper/2311.16172/) (2023)
- [Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report](https://mlsanity.com/paper/2412.14085/) (2024)

## Abstract (arXiv)

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.