AAAI Publications, Thirtieth AAAI Conference on Artificial Intelligence

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Using Domain Knowledge to Improve Monte-Carlo Tree Search Performance in Parameterized Poker Squares
Robert Arrington, Clay Langley, Steven Bogaerts

Last modified: 2016-03-05

Abstract


Poker Squares is a single-player card game played on a 5 x 5 grid, in which a player attempts to create as many high-scoring Poker hands as possible. As a stochastic single-player game with an extremely large state space, this game offers an interesting area of application for Monte-Carlo Tree Search (MCTS). This paper describes enhancements made to the MCTS algorithm to improve computer play, including pruning in the selection stage and a greedy simulation algorithm. These enhancements make extensive use of domain knowledge in the form of a state evaluation heuristic. Experimental results demonstrate both the general efficacy of these enhancements and their ideal parameter settings.

Keywords


Monte-Carlo Tree Search; poker squares; domain knowledge; heuristic

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