This sort of research is maybe less flashy than say using machine learning to automatically generate game assets from photos, but I think this sort of computer-aided game design is possibly the biggest way machine learning will transform video games. As games are becoming bigger and more complicated, the problem of tuning various gameplay parameters explodes exponentially. And, this sort of tuning can have a huge effect on player retention and overall game quality.
In this research the machine learning is being used to balance the game across different asymmetric strategies (different decks in the card game), but you could imagine using similar techniques for balancing and tuning content for single player games as well. Once you have a reasonable model of the player's behavior, you can do all sorts of automatic tuning like balancing the difficulty of jumps in a platformer, tuning enemy positions in an FPS, etc.
In this research the machine learning is being used to balance the game across different asymmetric strategies (different decks in the card game), but you could imagine using similar techniques for balancing and tuning content for single player games as well. Once you have a reasonable model of the player's behavior, you can do all sorts of automatic tuning like balancing the difficulty of jumps in a platformer, tuning enemy positions in an FPS, etc.