loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Garrett Nicolai 1 and Robert Hilderman 2

Affiliations: 1 Dalhousie University, Canada ; 2 University of Regina, Canada

ISBN: 978-989-8425-31-7

Keyword(s): Poker, Evolutionary algorithms, Evolutionary neural networks.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Co-Evolution and Collective Behavior ; Computational Intelligence ; Concurrent Co-Operation ; Enterprise Information Systems ; Evolution Strategies ; Evolutionary Computing ; Evolutionary Robotics and Intelligent Agents ; Game Theory Applications ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: Computers have difficulty learning how to play Texas Hold'em Poker. The game contains a high degree of stochasticity, hidden information, and opponents that are deliberately trying to mis-represent their current state. Poker has a much larger game space than classic parlour games such as Chess and Backgammon. Evolutionary methods have been shown to find relatively good results in large state spaces, and neural networks have been shown to be able to find solutions to non-linear search problems. In this paper, we present several algorithms for teaching agents how to play No-Limit Texas Hold'em Poker using a hybrid method known as evolving neural networks. Furthermore, we adapt heuristics such as halls of fame and co-evolution to be able to handle populations of Poker agents, which can sometimes contain several hundred opponents, instead of a single opponent. Our agents were evaluated against several benchmark agents. Experimental results show the overall best performance was obtained by an agent evolved from a single population (i.e., with no co-evolution) using a large hall of fame. These results demonstrate the effectiveness of our algorithms in creating competitive No-Limit Texas Hold'em Poker agents. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.81.29.226

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Nicolai, G. and Hilderman, R. (2010). ALGORITHMS FOR EVOLVING NO-LIMIT TEXAS HOLD'EM POKER PLAYING AGENTS.In Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010) ISBN 978-989-8425-31-7, pages 20-32. DOI: 10.5220/0003063000200032

@conference{icec10,
author={Garrett Nicolai. and Robert Hilderman.},
title={ALGORITHMS FOR EVOLVING NO-LIMIT TEXAS HOLD'EM POKER PLAYING AGENTS},
booktitle={Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010)},
year={2010},
pages={20-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003063000200032},
isbn={978-989-8425-31-7},
}

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010)
TI - ALGORITHMS FOR EVOLVING NO-LIMIT TEXAS HOLD'EM POKER PLAYING AGENTS
SN - 978-989-8425-31-7
AU - Nicolai, G.
AU - Hilderman, R.
PY - 2010
SP - 20
EP - 32
DO - 10.5220/0003063000200032

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.