ML Anthology

Using Domain Knowledge to Improve Monte-Carlo Tree Search Performance in Parameterized Poker Squares

Robert Arrington, Clay Langley, Steven A. Bogaerts
AAAI 2016 pp. 4065-4070
doi:10.1609/AAAI.V30I1.9852 /aaai/2016/arrington2016aaai-using/

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.

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Text

Arrington et al. "Using Domain Knowledge to Improve Monte-Carlo Tree Search Performance in Parameterized Poker Squares." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.9852

Markdown

[Arrington et al. "Using Domain Knowledge to Improve Monte-Carlo Tree Search Performance in Parameterized Poker Squares." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/arrington2016aaai-using/) doi:10.1609/AAAI.V30I1.9852

BibTeX

@inproceedings{arrington2016aaai-using,
  title     = {{Using Domain Knowledge to Improve Monte-Carlo Tree Search Performance in Parameterized Poker Squares}},
  author    = {Arrington, Robert and Langley, Clay and Bogaerts, Steven A.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2016},
  pages     = {4065-4070},
  doi       = {10.1609/AAAI.V30I1.9852},
  url       = {https://mlanthology.org/aaai/2016/arrington2016aaai-using/}
}