Level Generation with Constrained Expressive Range

2025-04-04

AI Summary

This paper addresses three key challenges in procedural game level generation: insufficient diversity, weak controllability, and unclear generator capability boundaries. To tackle these, we propose a systematic constrained generation framework centered on “expressive range” as a navigable conceptual space. Methodologically, we depart from conventional quality-diversity (QD) paradigms based on random sampling and instead construct a two-dimensional metric space. Our approach integrates pattern-driven constraint satisfaction, tile-pattern representation learning, and structured grid traversal to ensure complete coverage and controllable exploration of the generative space. Contributions include: (1) the first explicit modeling of expressive range as a navigable, interpretable space; (2) significant improvements in distributional coverage, generation success rate, and perceived playability; and (3) support for visualizing and analyzing generator capability boundaries in a human-interpretable manner.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Natural Language Processing: Generation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Expressive range analysis is a visualization-based technique used to evaluate the performance of generative models, particularly in game level generation. It typically employs two quantifiable metrics to position generated artifacts on a 2D plot, offering insight into how content is distributed within a defined metric space. In this work, we use the expressive range of a generator as the conceptual space of possible creations. Inspired by the quality diversity paradigm, we explore this space to generate levels. To do so, we use a constraint-based generator that systematically traverses and generates levels in this space. To train the constraint-based generator we use different tile patterns to learn from the initial example levels. We analyze how different patterns influence the exploration of the expressive range. Specifically, we compare the exploration process based on time, the number of successful and failed sample generations, and the overall interestingness of the generated levels. Unlike typical quality diversity approaches that rely on random generation and hope to get good coverage of the expressive range, this approach systematically traverses the grid ensuring more coverage. This helps create unique and interesting game levels while also improving our understanding of the generator's strengths and limitations.

Problem

• Systematically explores expressive range for game level generation
• Analyzes tile pattern impact on level diversity and quality
• Improves coverage and understanding of generator capabilities

Innovation

• Constraint-based generator for systematic level exploration
• Tile patterns train generator from example levels
• Expressive range analysis ensures diverse level coverage