Learning and Using Hand Abstraction Values for Parameterized Poker Squares

Publication Year:
2016
Usage 12
Abstract Views 12
Repository URL:
https://cupola.gettysburg.edu/csfac/32
Author(s):
Neller, Todd W.; Messinger, Colin M.; Yang, Zuozhi
Tags:
poker; artificial intelligence; poker squares; time management strategies; Artificial Intelligence and Robotics; Theory and Algorithms
conference paper description
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.