Automated Game Design via Conceptual Expansion
This addresses the challenge of automated game design for AI researchers, offering a novel approach that avoids reliance on hand-authored knowledge, though it appears incremental as it focuses on recreating existing games rather than creating entirely new ones.
The paper tackles automated game design by introducing a method that uses machine learning to learn representations of games and recombine them to create new games via conceptual expansion, demonstrating its ability to recreate existing games.
Automated game design has remained a key challenge within the field of Game AI. In this paper, we introduce a method for recombining existing games to create new games through a process called conceptual expansion. Prior automated game design approaches have relied on hand-authored or crowd-sourced knowledge, which limits the scope and applications of such systems. Our approach instead relies on machine learning to learn approximate representations of games. Our approach recombines knowledge from these learned representations to create new games via conceptual expansion. We evaluate this approach by demonstrating the ability for the system to recreate existing games. To the best of our knowledge, this represents the first machine learning-based automated game design system.