Decision Generalisation from Game Logs in No Limit Texas Hold'em
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 3062-3065.
Abstract
Given a set of data, recorded by observing the decisions of an expert player, we present a case-based framework that allows the successful generalisation of those decisions in the game of no limit Texas Hold'em. We address the problems of determining a suitable action abstraction and the resulting state translation that is required to map real-value bet amounts into a discrete set of abstract actions. We also detail the similarity metrics used in order to identify similar scenarios, without which no generalisation of playing decisions would be possible. We show that we were able to successfully generalise no limit betting decisions from recorded data via our agent, SartreNL, which achieved a 5th place finish out of 11 opponents at the 2012 Annual Computer Poker Competition.
@inproceedings{ijcai2013p457,
title = {Decision Generalisation from Game Logs in No Limit Texas Hold'em},
author = {Rubin, Jonathan and Watson, Ian D.},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {3062--3065},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/457},
}
TY - CPAPER TI - Decision Generalisation from Game Logs in No Limit Texas Hold'em AU - Rubin, Jonathan AU - Watson, Ian D. T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 3062 EP - 3065 UR - https://ijcai.org/proceedings/2013/457 ER -Copied
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