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Permanent link (DOI): https://doi.org/10.7939/R3S756T30

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Automated Abstraction of Large Action Spaces in Imperfect Information Extensive-Form Games Open Access

Descriptions

Other title
Subject/Keyword
Computer Poker
Artificial Intelligence
Multiagent Learning
Game Theory
No-limit poker
Abstraction
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Hawkin, John A
Supervisor and department
Szafron, Duane (Computing Science)
Holte, Robert (Computing Science)
Examining committee member and department
Holte, Robert (Computing Science)
Bowling, Michael (Computing Science)
Watson, Ian (Computer Science)
Messinger, Paul (School of Business)
Szafron, Duane (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2014-09-26T15:55:12Z
Graduation date
2014-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
An agent in an adversarial, imperfect information environment must sometimes decide whether or not to take an action and, if they take the action, must choose a parameter value associated with that action. Examples include choosing to buy or sell some amount of resources or choosing whether or not to move combined with a distance for that movement. This problem can be expanded to allow for mixing between multiple actions each with distinct associated parameter values and can be expressed as an imperfect information extensive form game. This dissertation describes a new automated method of abstracting the action space of such decision problems. It presents new algorithms for implementing the method, provides some theory about how the method relates to Nash equilibria in a small poker game and assesses the method using several poker games. One of these algorithms was used in the creation of an agent that won one of the divisions of the 2012 Annual Computer Poker Competition. An improvement upon this algorithm produced an action abstraction of two-player no-limit Texas hold'em poker that out-performs a state-of-the-art action abstraction while requiring less than 40% of the memory. The resulting agent had the best overall results in a round robin tournament of six top two-player no-limit Texas hold'em agents.
Language
English
DOI
doi:10.7939/R3S756T30
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
J. Hawkin, R. Holte and D. Szafron, Using sliding windows to generate action abstractions in extensive-form games, Proceedings of Twenty-Sixth National Conference on Artificial Intelligence (AAAI'12), Toronto, Canada, July, 2012, 1924-1930.J. Hawkin, R. Holte and D. Szafron, Automated Action Abstraction of Imperfect Information Extensive-Form Games, Proceedings of Twenty-Fifth National Conference on Artificial Intelligence (AAAI'11), San Francisco, USA, August, 2011, 681-687.

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