New computer algorithm may be unbeatable at poker

WASHINGTON: Scientists have developed an 'essentially unbeatable' computer algorithm that can play a virtually perfect game of poker - including bluffing.

The poker programme is "incapable of losing against any opponent in a fair game," according to its creator computer scientist Michael Bowling and his colleagues at the University of Alberta who worked with Finnish software developer Oskari Tammelin.

The algorithm has successfully solved a particular variant of poker, called heads-up limit hold'em (HULHE), 'nature.com' reported.

"Poker has been a challenge problem for artificial intelligence going back over 40 years, and until now, heads-up limit Texas hold'em poker was unsolved," said Bowling.

Poker is a family of games that exhibit imperfect information, where players do not have full knowledge of past events. The most popular variant of poker today is Texas hold'em.

When it is played with just two-players (heads-up) and with fixed bet-sizes and number of raises (limit), it is called heads-up limit hold'em.

While smaller than checkers, the imperfect information nature of heads-up limit hold'em makes it a far more challenging game for computers to play or solve.

"The breakthroughs behind this result are general algorithmic advances that make game-theoretic reasoning in large-scale models of any sort more tractable," said Bowling.

Bowling and colleagues designed their algorithm so that it would learn from experience, getting to its champion-level skills required playing more than 1,500 games.

At the beginning, it made its decisions randomly, but then it updated itself by attaching a 'regret' value to each decision, depending on how poorly it fared.

As part of its developing strategy, the computer learned to inject a certain dose of bluffing into its plays.

Although bluffing seems like a very human, psychological element of the game, it is in fact part of game theory - and, typically, of computer poker, researchers said.

Bowling said that the approach might be useful in real-life situations when one has to make decisions with incomplete information - for example, for managing a portfolio of investments.

The team is now focusing on applying their approach to medical decision-making, in collaboration with diabetes specialists.