Embedded Mechanics Generation

Abstract

Developing game mechanics is challenging due to the need for intricate design and programming. Procedural Content Generation (PCG) is a prevalent aspect of modern video game development, enabling the generation of content via algorithms. Achieving the desired balance and player experience is a multifaceted challenge, with game mechanics playing a crucial role—requiring thorough testing, player feedback, and iterative refinement. This work explores automated approaches to mechanic generation and evaluation, drawing from Automated Game Design (AGD). I present methods for generating mechanics, reconstructing levels through level inpainting, and creating enemies that can only be defeated using newly generated mechanics. Comparative studies between reinforcement learning agents and traditional static agents such as A* show that RL facilitates more diverse and human-like mechanic discovery, while static methods remain more stable but less creative. Ongoing work integrates these techniques into environments where mechanics, levels, and enemies co-evolve, enabling richer evaluation of gameplay dynamics. To assess alignment between generated content and designer intent, I propose Design Impact Accuracy (DIA) as a metric to measure how effectively new mechanics are supported within AI-generated levels and enemies.