To master the game of poker, one must be adaptive. Any
form of deterministic play can and will be exploited by a
good opponent. A player must change their style based on
the dynamic game conditions observed over a series of
hands (looking at each hand in isolation is an artificial
limitation). Our work has made some progress towards
achieving a poker-playing program that can learn and
adapt. Loki successfully uses opponent modeling to
improve its play. However, it is abundantly clear that
these are only the first steps, and there is considerable
room for improvement.

Poker is a complex game. Strong play requires the player
to excel in many different aspects of the game.
Developing Loki has been a cyclic process. We improve
one aspect of the program until it becomes apparent that
another aspect is the performance bottleneck. That
problem is then tackled until it is no longer the limiting
factor, and new weaknesses in the program’s play are
revealed. We made our initial foray into opponent
modeling and were pleased with the results. With the
success of the new simulation-based betting strategy,
opponent modeling is now back on the critical path since
it will offer the biggest performance gains. We will now
refocus our efforts on that topic, until it too moves off the
critical path.

Acknowledgments
This research was supported by the Natural Sciences and
Engineering Council of Canada.