A Memory-Based Approach to Two-Player Texas Hold'em

Authors:
Jonathan Rubin;Ian Watson
Affiliations:
Department of Computer Science, University of Auckland, New Zealand;Department of Computer Science, University of Auckland, New Zealand
Venue:
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Year:
2009

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Abstract

A Case-Based Reasoning system, nicknamed SARTRE, that uses a memory-based approach to play two-player, limit Texas Hold'em is introduced. SARTRE records hand histories from strong players and attempts to re-use this information to handle novel situations. SARTRE'S case features and their representations are described, followed by the results obtained when challenging a world-class computerised opponent. Our experimental methodology attempts to address how well SARTRE'S performance can approximate the performance of the expert player, who SARTRE originally derived the experience-base from.