Monte-Carlo Tree Search Used for Fortification in the Game of Risk

University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS); KTH/Skolan för elektroteknik och datavetenskap (EECS)

Abstract: The strategy game Risk is a very popular boardgame, requiring little effort to learn but lots of skill to master.The aim of this project is to explore the fortification phase of thegame, where the player’s troops are moved between territories.Our method is based on adapting Monte Carlo tree search(MCTS) to Risk. To improve the troop movements, we proposetwo techniques, hierarchical search and progressive bias. Thesemethods, combined with other extensions of MCTS are thencompared against a baseline player of the game. Our results showthat hierarchical search improved the MCTS agent’s playingpower and the progressive bias have potential to improve theagent but needs further investigation.

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