TAG: Terraforming Mars


  • Raluca D. Gaina Queen Mary University of London
  • James Goodman Queen Mary University of London
  • Diego Perez-Liebana Queen Mary University of London




Boardgame, Terraforming Mars, Artificial Intelligence, Game-playing, Board Games, Monte Carlo Tree Search, Rolling Horizon Evolutionary Algorithms, Game Analysis


Games and Artificial Intelligence (AI) have had a tight relationship for many years. A multitude of games have been used as environments in which AI players can learn to act and interact with others or the game mechanics directly; used as optimisation problems; used as generators of large amounts of data which can be analysed to learn about the game, or about the players; or used as containers of content which can be automatically generated by AI methods. Yet many of these environments have been very simple and limited in scope. We propose here a much more complex environment based on the boardgame Terraforming Mars, implemented as part of the Tabletop Games Framework: a very large and dynamic action space, hidden information, large amounts of content, resource management and high variability make this problem domain stand out in the current landscape and a very interesting problem for AI methods of multiple domains. We include results of baseline AI game-players in this game and in-depth analysis of the game itself, together with an exploration of problem complexity, challenges and opportunities.




How to Cite

Gaina, R. D., Goodman, J., & Perez-Liebana, D. (2021). TAG: Terraforming Mars. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 17(1), 148-155. https://doi.org/10.1609/aiide.v17i1.18902