Ludus: An Optimization Framework to Balance Auto Battler Cards


  • Nathaniel Budijono University of Minnesota Twin Cities
  • Phoebe Goldman New York University
  • Jack Maloney University of Wisconsin-Madison
  • Joseph B. Mueller Smart Information Flow Technologies (SIFT) University of Minnesota
  • Phillip Walker Smart Information Flow Technologies (SIFT)
  • Jack Ladwig Smart Information Flow Technologies (SIFT)
  • Richard G. Freedman Smart Information Flow Technologies (SIFT)



Automated Game Design, Global Search, Optimization, Auto Battler


Auto battlers are a recent genre of online deck-building games where players choose and arrange cards that then compete against other players' cards in fully-automated battles. As in other deck-building games, such as trading card games, designers must balance the cards to permit a wide variety of competitive strategies. We present Ludus, a framework that combines automated playtesting with global search to optimize parameters for each card that will assist designers in balancing new content. We develop a sampling-based approximation to reduce the playtesting needed during optimization. To guide the global search, we define metrics characterizing the health of the metagame and explore their impacts on the results of the optimization process. Our research focuses on an auto battler game we designed for AI research, but our approach is applicable to other auto battler games.




How to Cite

Budijono, N., Goldman, P., Maloney, J., Mueller, J. B., Walker, P., Ladwig, J., & Freedman, R. G. (2022). Ludus: An Optimization Framework to Balance Auto Battler Cards. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12727-12734.