Learning and Using Hand Abstraction Values for Parameterized Poker Squares
DOI:
https://doi.org/10.1609/aaai.v30i1.9859Keywords:
game artificial intelligence, reinforcement learning, expectimaxAbstract
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 signicantly accounted for observed performance.
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Published
2016-03-05
How to Cite
Neller, T., Messinger, C., & Yang, Z. (2016). Learning and Using Hand Abstraction Values for Parameterized Poker Squares. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9859
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Section
EAAI Symposium Full Paper