  The act of bluffing confounds game designers to this day. The very nature of
bluffing is even open for debate, adding further complication to the process of
creating intelligent virtual players that can bluff, and hence play,
realistically. Through the use of intelligent, learning agents, and carefully
designed agent outlooks, an agent can in fact learn to predict its opponents
reactions based not only on its own cards, but on the actions of those around
it. With this wider scope of understanding, an agent can in learn to bluff its
opponents, with the action representing not an illogical action, as bluffing is
often viewed, but rather as an act of maximising returns through an effective
statistical optimisation. By using a tee dee lambda learning algorithm to
continuously adapt neural network agent intelligence, agents have been shown to
be able to learn to bluff without outside prompting, and even to learn to call
each others bluffs in free, competitive play.
