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ACG
2009
Springer

Monte-Carlo Tree Search in Settlers of Catan

9 years 9 months ago
Monte-Carlo Tree Search in Settlers of Catan
Abstract. Games are considered important benchmark tasks of 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 considerable playing strength when compared to existing heuristics for the game. We conclude that MCTS is suitable for implementing a strong Settlers of Catan player...
Istvan Szita, Guillaume Chaslot, Pieter Spronck
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where ACG
Authors Istvan Szita, Guillaume Chaslot, Pieter Spronck
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