Learning and Using Hand Abstraction Values for Parameterized Poker Squares

Authors

  • Todd Neller Gettysburg College
  • Colin Messinger Gettysburg College
  • Zuozhi Yang Gettysburg College

DOI:

https://doi.org/10.1609/aaai.v30i1.9859

Keywords:

game artificial intelligence, reinforcement learning, expectimax

Abstract

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.

AAAI 2016 Proceedings Cover

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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

Issue

Vol. 30 No. 1 (2016): Thirtieth AAAI Conference on Artificial Intelligence

Section

EAAI Symposium Full Paper