  A defining feature of collectable card games is the deck building process
prior to actual gameplay, in which players form their decks according to some
restrictions. Learning to build decks is difficult for players and models alike
due to the large card variety and highly complex semantics, as well as
requiring meaningful card and deck representations when aiming to utilise AI.
In addition, regular releases of new card sets lead to unforeseeable
fluctuations in the available card pool, thus affecting possible deck
configurations and requiring continuous updates. Previous Game AI approaches to
building decks have often been limited to fixed sets of possible cards, which
greatly limits their utility in practice. In this work, we explore possible
card representations that generalise to unseen cards, thus greatly extending
the real-world utility of AI-based deck building for the game "Magic: The
Gathering".We study such representations based on numerical, nominal, and
text-based features of cards, card images, and meta information about card
usage from third-party services. Our results show that while the particular
choice of generalised input representation has little effect on learning to
predict human card selections among known cards, the performance on new, unseen
cards can be greatly improved. Our generalised model is able to predict 55\% of
human choices on completely unseen cards, thus showing a deep understanding of
card quality and strategy.
