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Many senses, smell, touch, hearing, and sight, can potentially be augmented, though the most common application of AR is sight, using a head-mounted display [2]. Several users may simultaneously access and operate a shared digitally augmented environment, either at the same place or remotely. Users commonly interact with each other and the augmented elements in this virtual framework by using hand gestures, movement, and even gaze. The interactive nature of AR, as well as its direct connection to the real world, have produced extensive research work and industrial applications of AR to different fields such as education, entertainment, medicine, and retail [6]. Human-Computer interaction in games (HCI-games) is a very broad field that covers research on the many ways in which human players interact with digital games that, given their interactive, playful, and challenging nature, present a rich field of study separated from human-computer interaction in other forms of software [1].

Monte Carlo Tree Search (MCTS) is a relatively new sampling method with multiple variants in the literature. They can be applied to a wide variety of challenging domains including board games, video games, and energy-based problems to mention a few. In this work, we explore the use of the vanilla MCTS and the MCTS with Rapid Action Value Estimation (MCTS-RAVE) in the game of Carcassonne, a stochastic game with a deceptive scoring system where limited research has been conducted. We compare the strengths of the MCTS-based methods with the Star2.5 algorithm, previously reported to yield competitive results in the game of Carcassonne when a domain-specific heuristic is used to evaluate the game states. We analyse the particularities of the strategies adopted by the algorithms when they share a common reward system. The MCTS-based methods consistently outperformed the Star2.5 algorithm given their ability to find and follow long-term strategies, with the vanilla MCTS exhibiting a more robust game-play than the MCTS-RAVE.