Automated Game Design via Conceptual Expansion
  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.
