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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2015: Current Approaches in Applied Artificial Intelligence pp 43–52Cite as

AIs for Dominion Using Monte-Carlo Tree Search

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9101)

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.

Keywords

  • Dominion
  • MCTS
  • UCB
  • UCT

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Author information

Authors and Affiliations

  1. Department of ICT, University of Agder, Grimstad, Norway

    Robin Tollisen, Jon Vegard Jansen, Morten Goodwin & Sondre Glimsdal

Authors
  1. Robin Tollisen
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  2. Jon Vegard Jansen
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  3. Morten Goodwin
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  4. Sondre Glimsdal
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Corresponding author

Correspondence to Jon Vegard Jansen .

Editor information

Editors and Affiliations

  1. Texas State University, San Marcos, Texas, USA

    Moonis Ali

  2. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Young Sig Kwon

  3. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Chang-Hwan Lee

  4. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Juntae Kim

  5. Seoul National University, Seoul, Korea, Republic of (South Korea)

    Yongdai Kim

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19065-5

  • Online ISBN: 978-3-319-19066-2

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