Roles
Student Authors:
Zuozhi Yang '17, Gettysburg College
Colin M. Messinger '17, Gettysburg College
Document Type
Conference Material
Publication Date
6-29-2016
Department 1
Computer Science
Abstract
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.
Copyright Note
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.
DOI
10.1007/978-3-319-50935-8_3
Recommended Citation
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.
https://cupola.gettysburg.edu/csfac/35
Required Publisher's Statement
Original version available online at https://link.springer.com/chapter/10.1007%2F978-3-319-50935-8_3
Comments
Presented during the 9th International Conference on Computers and Games on June 29th, 2016.