2019/06/08 by Ganzfried, Sam, Chiswick, Max
#Artificial Intelligence (cs.AI) #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Theoretical Economics (econ.TH)
paper · doi:10.48550/arxiv.1906.09895
Poker is a large complex game of imperfect information, which has been singled out as a major AI challenge problem. Recently there has been a series of breakthroughs culminating in agents that have successfully defeated the strongest human players in two-player no-limit Texas hold 'em. The strongest agents are based on algorithms for approximating Nash equilibrium strategies, which are stored in massive binary files and unintelligible to humans. A recent line of research has explored approaches for extrapolating knowledge from strong game-theoretic strategies that can be understood by humans. This would be useful when humans are the ultimate decision maker and allow humans to make better decisions from massive algorithmically-generated strategies. Using techniques from machine learning we have uncovered a new simple, fundamental rule of poker strategy that leads to a significant improvement in performance over the best prior rule and can also easily be applied by human players.