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.
Probabilistic models were fit to logs of player actions in the card game Dominion in an attempt to find evidence of personality types that could be used to classify player behavior as well as generate probabilistic bot behavior. Expectation Maximization seeded with players' self-assessments for their motivations was run for two different model types - Naive Bayes and a trigram model - to uncover three clusters each. For both model structures, most players were classified as belonging to a single large cluster that combined the goals of splashy plays, clever combos, and effective play, cross-cutting the original categories - a cautionary tale for research that assumes players can be classified into one category or another. However, subjects qualitatively report that the different model structures play very differently, with the Naive Bayes model more creatively combining cards.