15 research outputs found
Most Important Fundamental Rule of Poker Strategy
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
Bayesian Opponent Modeling in Multiplayer Imperfect-Information Games
In many real-world settings agents engage in strategic interactions with
multiple opposing agents who can employ a wide variety of strategies. The
standard approach for designing agents for such settings is to compute or
approximate a relevant game-theoretic solution concept such as Nash equilibrium
and then follow the prescribed strategy. However, such a strategy ignores any
observations of opponents' play, which may indicate shortcomings that can be
exploited. We present an approach for opponent modeling in multiplayer
imperfect-information games where we collect observations of opponents' play
through repeated interactions. We run experiments against a wide variety of
real opponents and exact Nash equilibrium strategies in three-player Kuhn poker
and show that our algorithm significantly outperforms all of the agents,
including the exact Nash equilibrium strategies