Procedural Content Generation via Machine Learning (PCGML) has enhanced game content creation, yet challenges in controllability and limited training data persist. This study addresses these issues by distilling a constructive PCG algorithm into a controllable PCGML model. We first generate a large amount of content with a constructive algorithm and label it using a Large Language Model (LLM). We use these synthetic labels to condition two PCGML models for content-specific generation, a diffusion model and the five-dollar model. This neural network distillation process ensures that the generation aligns with the original algorithm while introducing controllability through plain text. We define this text-conditioned PCGML as a Text-to-game-Map (T2M) task, offering an alternative to prevalent text-to-image multi-modal tasks. We compare our distilled models with the baseline constructive algorithm. Our analysis of the variety, accuracy, and quality of our generation demonstrates the efficacy of distilling constructive methods into controllable text-conditioned PCGML models.

How can we use generative AI, in particular textguided generation of key game content, be used as a core game mechanic? This paper describes the design, implementation, and and initial playtesting of a game designed around prompt-driven map generation. Our game God's Innovation Project - a god game in the tradition of Populous - allows the player to reshape parts of the game world with a promptable generative model. We further gamify the prompting process, creating a novel resource system where words must harvested via gameplay. We explore how recent advances in the field of Procedural Content Generation via Machine learning enable new types of AI-based game design, and assess the impact of AI on the gameplay experience using a small-scale playtest.

The Connections puzzle published each day by the New York Times tasks players with dividing a bank of sixteen words into four groups of four words that each relate to a common theme. Solving the puzzle requires both common linguistic knowledge (i.e. definitions and typical usage) as well as, in many cases, lateral or abstract thinking. This is because the four categories ascend in complexity, with the most challenging category often requiring thinking about words in uncommon ways or as parts of larger phrases. We investigate the capacity for automated AI systems to play Connections and explore the game’s potential as an automated benchmark for abstract reasoning and a way to measure the semantic information encoded by data-driven linguistic systems. In particular, we study both a sentence-embedding baseline and modern large language models (LLMs). We report their accuracy on the task, measure the impacts of chain-of-thought prompting, and discuss their failure modes. Overall, we find that the Connections task is challenging yet feasible, and a strong test-bed for future work.

The open-world sandbox game Minecraft is well-known for applying a wide array of procedural content generation techniques to create unique and expansive game environments. However, procedurally generated buildings are absent in the Minecraft world, thus players must build their own structures to flesh out their worlds. This build process can be extremely time-consuming and appeals to more creatively-inclined players. To aid players in this process, we introduce a tool combining interactive evolution with latent variable evolution to evolve procedurally generated Minecraft structures to a player’s aesthetic choices. We employ two separate neural network models to generate structures: a 3D generative model for generating the structure design and an encoding model for applying Minecraft textures to the structure’s voxels. We evaluate this tool with a user study incorporating an online interface that allows participants to select, evolve, and guide a population of these generated 3D structures towards a specific design goal.