Estimating the Odds for Texas Hold´em Poker Agents

Developing software agents that play incomplete information games is a demanding task: it is required they incorporate strategies capable of dealing with hidden information and deception and risk management. In Poker, these issues are commonly addressed by estimating opponents´ game play using a variety of techniques such as Expected Hand Strength (E[HS]) or Hand Potential. In this paper, we propose criteria which can be applied when assessing such techniques, and we have also run benchmark tests which demonstrate their pertinence. We have, however, been faced with a clear gap in terms of the methods´ efficiency. While this is not a problem in theoretical models, when implementing such methods in real world applications, they can prove to be painfully slow. In order to address this issue, we propose the Average Rank Strength (ARS) method. It can calculate the strength of a hand of any size through the hand´s rank width negligible error, when compared to the original method. Still, the greatest contribution of this method lies in the speed-up factor of about 1000 times over E[HS]. Since most successful agents in the literature use their game abstraction based on E[HS], this breakthrough will significantly contribute towards a much lighter strategy computation, since this routine must be called billions of times. By saving computation time, we believe that future integration of ARS with current game playing algorithms will allow for creating agents with smaller abstraction levels, thus making room for improvement in their overall performance.