  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.
