Abstract
Dominion is a complex game, with hidden information and stochastic elements. This makes creating any artificial intelligence (AI) challenging. To this date, there is little work in the literature on AI for Dominion, and existing solutions rely upon carefully tuned finite-state solutions.
This paper presents two novel AIs for Dominion based on Monte-Carlo Tree Search (MCTS) methods. This is achieved by employing Upper Confidence Bounds (UCB) and Upper Confidence Bounds applied to Trees (UCT). The proposed solutions are notably better than existing work. The strongest proposal is able to win 67% of games played against a known, good finite-state solution, even when the finite-state solution has the unfair advantage of starting the game.
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Tollisen, R., Jansen, J.V., Goodwin, M., Glimsdal, S. (2015). AIs for Dominion Using Monte-Carlo Tree Search. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_5
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DOI: https://doi.org/10.1007/978-3-319-19066-2_5
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