The visual style of game elements considerably contributes to the overall experience. Aesthetics influence player appeal, while the abilities of game pieces define their in-game functionality. In this paper, we investigate how the visual style of collectible cards influences the players' perception of the card's actual strength in the game. Using the popular trading card game Magic: The Gathering, we conduct a single-blind survey study that examines how players perceive the strength of AI-generated cards that are shown in two contrasting visual styles: cute and harmless, or heroic and mighty. Our analysis reveals that some participants are influenced by a card's visual appearance when judging its in-game strength. Overall, differences in style perception are normally distributed around a neutral center, but individual participants vary in both directions: some generally perceive the cute style to be stronger, whereas others believe that the heroic style is better.

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.

In imperfect information games, the game state is generally not fully observable to players. Therefore, good gameplay requires policies that deal with the different information that is hidden from each player. To combat this, effective algorithms often reason about information sets; the sets of all possible game states that are consistent with a player's observations. While there is no way to distinguish between the states within an information set, this property does not imply that all states are equally likely to occur in play. We extend previous research on assigning weights to the states in an information set in order to facilitate better gameplay in the imperfect information game of Reconnaissance Blind Chess. For this, we train two different neural networks which estimate the likelihood of each state in an information set from historical game data. Experimentally, we find that a Siamese neural network is able to achieve higher accuracy and is more efficient than a classical convolutional neural network for the given domain. Finally, we evaluate an RBC-playing agent that is based on the generated weightings and compare different parameter settings that influence how strongly it should rely on them. The resulting best player is ranked 5thon the public leaderboard.

Research in Game Artificial Intelligence distinguishes between fully observable, perfect-information games and imperfect-information games, which hide part of the game’s full information. In games with imperfect information, all possible game states that are consistent with a player’s currently available information about the progress of the game are called the information set for that player. This information set can be used for multiple purposes such as determining the expected outcome of a certain move by evaluating it on all possible states in the information set. While in theory there is no way to distinguish states within an information set, players can use experience and other context information to estimate which states are the most likely. In this paper, we estimate a probability distribution over an information set from historic data such that we can assign a weight to each individual state. We achieve this by training a Siamese neural network with triplets of comparisons between different states in the information set given the context of the previously obtained information. A first evaluation in the game of Reconnaissance Blind Chess shows that we can learn to identify the one true game state in a large information set with high probability. In addition, when used within a naively constructed RBC agent, this approach shows promising gameplay performance. At the time of writing, a simple agent based on the Siamese neural network is ranked #6 of all agents on the public RBC leaderboard.

In this work, we adapt a training approach inspired by the original AlphaGo system to play the imperfect information game of Reconnaissance Blind Chess. Using only the observations instead of a full description of the game state, we first train a supervised agent on publicly available game records. Next, we increase the performance of the agent through self-play with the on-policy reinforcement learning algorithm Proximal Policy optimization. We do not use any search to avoid problems caused by the partial observability of game states and only use the policy network to generate moves when playing. With this approach, we achieve an ELO of 1330 on the RBC leaderboard, which places our agent at position 27 at the time of this writing. We see that self-play significantly improves performance and that the agent plays acceptably well without search and without making assumptions about the true game state.

Drafting, i.e., the iterative, adversarial selection of a subset of items from a larger candidate set, is a key element of many games and related problems. It encompasses team formation in sports or e-sports, as well as deck selection in formats of many modern card games. The key difficulty of drafting is that it is typically not sufficient to simply evaluate each item in a vacuum and to select the best items. The evaluation of an item depends on the context of the set of items that were already selected earlier, as the value of a set is not just the sum of the values of its members - it must include a notion of how well items go together. In this paper, we study drafting in the context of the card game Magic: The Gathering. We propose the use of the Contextual Preference Ranking framework, which learns to compare two possible extensions of a given deck of cards. We demonstrate that the resulting neural network is better able to better inform decisions in this game than previous attempts.