Monte-Carlo Tree Search in Settlers of Catan

  • István Szita
  • Guillaume Chaslot
  • Pieter Spronck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6048)


Games are considered important benchmark opportunities for artificial intelligence research. Modern strategic board games can typically be played by three or more people, which makes them suitable test beds for investigating multi-player strategic decision making. Monte-Carlo Tree Search (MCTS) is a recently published family of algorithms that achieved successful results with classical, two-player, perfect-information games such as Go. In this paper we apply MCTS to the multi-player, non-deterministic board game Settlers of Catan. We implemented an agent that is able to play against computer-controlled and human players. We show that MCTS can be adapted successfully to multi-agent environments, and present two approaches of providing the agent with a limited amount of domain knowledge. Our results show that the agent has a considerable playing strength when compared to game implementation with existing heuristics. So, we may conclude that MCTS is a suitable tool for achieving a strong Settlers of Catan player.


Domain Knowledge Reinforcement Learning Game Tree Playing Strength Simulated Game 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • István Szita
    • 1
  • Guillaume Chaslot
    • 1
  • Pieter Spronck
    • 2
  1. 1.Department of Knowledge EngineeringMaastricht University 
  2. 2.Tilburg centre for Creative ComputingTilburg University 

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