Clustering Player Strategies from Variable-Length Game Logs in Dominion
  We present a method for encoding game logs as numeric features in the card
game Dominion. We then run the manifold learning algorithm t-SNE on these
encodings to visualize the landscape of player strategies. By quantifying game
states as the relative prevalence of cards in a player's deck, we create
visualizations that capture qualitative differences in player strategies.
Different ways of deviating from the starting game state appear as different
rays in the visualization, giving it an intuitive explanation. This is a
promising new direction for understanding player strategies across games that
vary in length.
