Dominion: A New Frontier for AI Research

Games have long played a role in AI research, both as a test-bed, and as a moving goal-post, constantly driving innovation. From the heyday of chess agents, when Deep Blue beat Gary Kasparov, to more recent advances, like AlphaGo's dark horse ascent to fame, games have both assisted AI research and provided something to aim for. As the AIs got better, the games they were applied to also got more complex. New game mechanics, such as the fog of war in StarCraft and the stochasticity of Poker, pushed researchers to adapt their methods to ever greater generality. In this paper, we argue that the deck-building strategy game Dominion [1] deserves to join the ranks of AI benchmark games, providing an RL-based bot in service of that benchmark. Dominion has all of the abovementioned elements, but it also incorporates a mechanic that is not present in other popular RL benchmarks: every game is played with a different set of cards. Since each dominion card has a specific rule printed on it, and the set of 10 cards for a game are randomly picked from among hundreds of cards, no two games of Dominion can be approached the same way. Thus a key part of playing Dominion is adapting one's inductive bias of how to play to the specific cards on the table.