‘Journeys in the Dark’ - Towards Game Master AI in Complex Board Games

The Game Master is a player role synonymous with many tabletop games. The asymmetric gameplay of the role provides different opportunities compared to other players, and can be both cooperative and competitive with the other players in the same game. Though complex environments for exploring human and Artificial Intelligence collaboration exist, few focus on the Game Master role's semi-cooperative play. Here, we propose a new complex environment based on the board game `Descent: Journeys in the Dark (Second Edition)', as part of the Tabletop Games Framework, showcasing one-versus-many play, tactical combat, and large, dynamic action and state spaces. We include baseline AI player performance of Monte Carlo Tree Search agents in this game, finding them to be well-adept at considering multiple possible end-game conditions compared to the greedy One Step Look Ahead agents. In-depth analysis reveals interesting behaviours and Hero synergies, with the aim of informing the design of games and AI models to enhance human experience in semi-cooperative environments.