Emulating human play in a leading mobile card game

Monte Carlo Tree Search (MCTS) has become a popular solution for game AI, capable of creating strong game playing opponents. However, the emergent playstyle of agents using MCTS is not neces- sarily human-like, believable or enjoyable. AI Factory Spades, currently the top rated Spades game in the Google Play store, uses a variant of MCTS to control AI allies and opponents. In collaboration with the developers, we showed in a previous study that the playstyle of human players significantly differed from that of the AI players [1]. This article presents a method for player modelling using gameplay data and neural networks that does not require domain knowledge, and a method of biasing MCTS with such a player model to create Spades playing agents that emulate human play whilst maintaining strong, competitive performance. The methods of player modelling and biasing MCTS presented in this study are applied to the commercial codebase of AI Factory Spades, and are transferable to MCTS implementations for discrete-action games where relevant gameplay data is available.