Monte Carlo Approaches to Parameterized Poker Squares
Zuozhi Yang '17, Gettysburg College
Colin M. Messinger '17, Gettysburg College
The paper summarized a variety of Monte Carlo approaches employed in the top three performing entries to the Parameterized Poker Squares NSG Challenge competition. In all cases AI players benefited from real-time machine learning and various Monte Carlo game-tree search techniques.
This is the author's version of the work. This publication appears in Gettysburg College's institutional repository by permission of the copyright owner for personal use, not for redistribution.
Neller, Todd W.; Yang, Zuozhi; Messinger, Colin M.; Anton, Calin; Castro-Wunsch, Karo; Maga, William; Bogaerts, Steven; Arrington, Robert; and Langely, Clay, "Monte Carlo Approaches to Parameterized Poker Squares" (2016). Computer Science Faculty Publications. 35.
Required Publisher's Statement
Original version available online at https://link.springer.com/chapter/10.1007%2F978-3-319-50935-8_3