Player Identification and Next-Move Prediction for Collectible Card Games with Imperfect Information

Abstract

Effectively identifying an individual and predicting their future actions is a material aspect of player analytics, with applications for player engagement and game security. Collectible card games are a fruitful test space for studying player identification, given that their large action spaces allow for flexibility in play styles, thereby facilitating behavioral analysis at the individual, rather than the aggregate, level. Further, once players are identified, modeling the differences between individuals may allow us to preemptively detect patterns that foretell future actions. As such, we use the virtual collectible card game "Legends of Code and Magic" to research both of these topics. Our main contributions to the task are the creation of a comprehensive dataset of Legends of Code and Magic game states and actions, extensive testing of the minimum information and computational methods necessary to identify an individual from their actions, and examination of the transferability of knowledge collected from a group to unknown individuals.