Monte Carlo search applied to card selection in Magic: The Gathering

We present the card game Magic: The Gathering as an interesting test bed for AI research. We believe that the complexity of the game offers new challenges in areas such as search in imperfect information domains and opponent modelling. Since there are a thousands of possible cards, and many cards change the rules to some extent, to successfully build AI for Magic: The Gathering ultimately requires a rather general form of game intelligence (although we only consider a small subset of these cards in this paper). We create a range of players based on stochastic, rule-based and Monte Carlo approaches and investigate Monte Carlo search with and without the use of a sophisticated rule-based approach to generate game rollouts. We also examine the effect of increasing numbers of Monte Carlo simulations on playing strength and investigate whether Monte Carlo simulations can enable an otherwise weak player to overcome a stronger rule-based player. Overall, we show that Monte Carlo search is a promising avenue for generating a strong AI player for Magic: The Gathering.