Improving Conditional Level Generation using Automated Validation in Match-3 Games

TL;DR

This paper introduces Avalon, a method that enhances match-3 level generation by conditioning on gameplay difficulty statistics, resulting in more valid and controllable levels while preserving stylistic features.

Contribution

It presents a novel difficulty-conditioned variational autoencoder for match-3 level generation, improving validity and style preservation over unconditioned models.

Findings

Generated levels are more valid with difficulty conditioning.

The approach preserves stylistic features of the dataset.

Conditioning improves control over level difficulty.

Abstract

Generative models for level generation have shown great potential in game production. However, they often provide limited control over the generation, and the validity of the generated levels is unreliable. Despite this fact, only a few approaches that learn from existing data provide the users with ways of controlling the generation, simultaneously addressing the generation of unsolvable levels. %One of the main challenges it faces is that levels generated through automation may not be solvable thus requiring validation. are not always engaging, challenging, or even solvable. This paper proposes Avalon, a novel method to improve models that learn from existing level designs using difficulty statistics extracted from gameplay. In particular, we use a conditional variational autoencoder to generate layouts for match-3 levels, conditioning the model on pre-collected statistics such as game mechanics like difficulty and relevant visual features like size and symmetry. Our method is general enough that multiple approaches could potentially be used to generate these statistics. We quantitatively evaluate our approach by comparing it to an ablated model without difficulty conditioning. Additionally, we analyze both quantitatively and qualitatively whether the style of the dataset is preserved in the generated levels. Our approach generates more valid levels than the same method without difficulty conditioning.