Poker Programming – Step 4 (Knowing the Game in Depth)


Poker Programming – Step 4 (Knowing the Game in Depth)

Poker provides an excellent benchmark to study and evaluate cognitive models intractable yet naturalistic settings that are simple and formal yet reproduce much of the complexity of real life. It is probably the most widely played card game, with endless variations played by millions of adherents from casual players gambling pennies to professionals competing in million-dollar tournaments. Unlike other games that emphasize one particular aspect of cognition, poker involves a broad range of cognitive activities, including:

1.Reasoning under uncertainty (opponents’ cards)
2.Dealing with probabilistic outcomes (future cards)
3.Decision-making with multiple options (chips used for bets)
4.Individual differences (different styles of play)
5.Inference of intent (from opponents’ bets)
6.Intentional deception (bluffing, sandbagging)
7.Pattern recognition (detecting trends from flow of game)
8.Economic behavior (factoring impact of amount of bets)

Because of the range of cognitive activities involved, poker provides a broader and more challenging test for cognitive modeling than other games such as chess that focus on a more restricted range of mechanisms (e.g. search). Despite the complexity of aspects involved, it remains a highly tractable domain, partly because it abstracts away from computationally demanding perception and interaction problems. Poker is increasingly being played in online gaming communities where the need for a challenging, cognitively plausible agents is increasing. Poker, therefore, provides a challenging domain at the intersection of fundamental research questions and
potential mass application.

To this end, it is desirable to build a no-limit poker game, playing Texas Hold’em, which displays evidence of these cognitive activities. Our project attempts to build such a poker application, with the hopes of competing with the best of poker platforms built to this date.