Learning and Using Hand Abstraction Values for Parameterized Poker Squares
Colin M. Messinger '17, Gettysburg College
Zuozhi Yang '17, Gettysburg College
We describe the experimental development of an AI player that adapts to different point systems for Parameterized Poker Squares. After introducing the game and research competition challenge, we describe our static board evaluation utilizing learned evaluations of abstract partial Poker hands. Next, we evaluate various time management strategies and search algorithms. Finally, we show experimentally which of our design decisions most significantly accounted for observed performance.
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, Colin Messinger, and Zuozhi Yang. "Learning and Using Hand Abstraction Values for Parameterized Poker Squares." Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16). Phoenix, AZ: 2016.
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Original version available from publisher at http://www.aaai.org/Press/Proceedings/aaai16.php