Group-Supervised Contrastive Learning for Semantic PCG Map Representations

Abstract

Learning general-purpose representations for symbolic procedural content generation (PCG) remains challenging due to the domain gap between discrete tile maps and natural images, and the lack of reusable semantic backbones for analyzing generated content. A key challenge in PCG is that diverse maps may share identical design intent when generated from the same control parameters, creating many-to-one semantic mappings. Standard contrastive learning approaches struggle in this setting, often treating semantically equivalent maps as negatives, leading to fragmented embedding spaces. In this paper, we present a controlled study on learning semantic representations for symbolic maps aligned with natural language. We introduce a group-supervised contrastive learning framework that addresses semantic ambiguity through two complementary mechanisms: (1) a data construction pipeline that expands parameter-based templates into diverse natural language descriptions using Large Language Models (LLMs), and (2) a group-supervised multi-positive contrastive objective that aligns text embeddings with sets of maps generated under shared control parameters. We evaluate our approach in a Zelda-based grid environment against visual (CLIP, ResNet) and statistical (Tile-MLP) baselines. Results show that explicitly modeling semantic groups leads to improved retrieval accuracy (60.8% R@5 versus 25.7% for statistical baselines), with larger gains under out-of-distribution settings. Moreover, the learned representations support zero-shot inference of several abstract gameplay attributes (e.g., solvability and threat level) within tile-aligned domains, without task-specific supervision. While cross-game transfer remains limited and sensitive to tile mapping quality and domain shift, these results suggest that parameter-guided semantic alignment is a practical step toward reusable representation backbones for symbolic PCG analysis within related domains.