Case-Based Reasoning for Adaptive Strategies in Texas Hold'em Poker

Most of the existing poker agents using case-based reasoning (CBR) are based on imitation of other poker agents and have weak capabilities of adapting their own strategies to different opponents or playing styles. We address these concerns in the development of UpperCase, a heads up no-limit Texas H...

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Bibliographic Details
Main Authors: Ommedal, Jan Berge, Solbakken, Eivind R
Format: Dissertation
Language:English
Subjects:
Intelligente systemer
MTDT datateknikk
ntnudaim:7467
Online Access:Request full text
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Summary:Most of the existing poker agents using case-based reasoning (CBR) are based on imitation of other poker agents and have weak capabilities of adapting their own strategies to different opponents or playing styles. We address these concerns in the development of UpperCase, a heads up no-limit Texas Hold'em poker agent representing a new approach to the application of CBR in poker. Using methods of perfect information hindsight analysis, the poker agent attempts to more accurately determine the quality of poker decisions. Through extensive exploration of the quality of different decisions, UpperCase is able to invent new poker strategies. The agent also tries to recognize different opponents by observing their actions and perform adaptation accordingly. Experimental results suggest that the agent is able to successfully create new profitable strategies, as well as achieve increased performance by dynamically changing its strategy during play.