Playing Carcassonne with Monte Carlo Tree Search

• URL: http://arxiv.org/abs/2009.12974v2
• Date: Sun, 4 Oct 2020 17:49:29 GMT
• Title: Playing Carcassonne with Monte Carlo Tree Search
• Authors: Fred Valdez Ameneyro, Edgar Galvan, Anger Fernando Kuri Morales
• Abstract summary: We explore the use of the vanilla Monte Carlo Tree Search (MCTS) and the Rapid Action Value Estimation (MCTS-RAVE) in the game of Carcassonne. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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