BeeMo, a Monte Carlo Simulation Agent for Playing Parameterized Poker Squares
Abstract
We investigated Parameterized Poker Squares to approximate an optimal game playing agent. We organized our inquiry along three dimensions: partial hand representation, search algorithms, and partial hand utility learning. For each dimension we implemented and evaluated several designs, among which we selected the best strategies to use for BeeMo, our final product. BeeMo uses a parallel flat Monte-Carlo search. The search is guided by a heuristic based on hand patterns utilities, which are learned through an iterative improvement method involving Monte-Carlo simulations and optimized greedy search.
Cite
Text
Castro-Wunsch et al. "BeeMo, a Monte Carlo Simulation Agent for Playing Parameterized Poker Squares." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.9856Markdown
[Castro-Wunsch et al. "BeeMo, a Monte Carlo Simulation Agent for Playing Parameterized Poker Squares." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/castrowunsch2016aaai-beemo/) doi:10.1609/AAAI.V30I1.9856BibTeX
@inproceedings{castrowunsch2016aaai-beemo,
title = {{BeeMo, a Monte Carlo Simulation Agent for Playing Parameterized Poker Squares}},
author = {Castro-Wunsch, Karo and Maga, William and Anton, Calin},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {4071-4074},
doi = {10.1609/AAAI.V30I1.9856},
url = {https://mlanthology.org/aaai/2016/castrowunsch2016aaai-beemo/}
}