Artificial intelligence milestone -Polaris computer begins beating human poker champions

The Second Man-Machine Poker Competition has computers from the University of Alberta playing some of the biggest names in the online poker world: Nick “stoxtrader” Grudzien, Matt “Hoss_TBF” Hawrilenko, and IJay “doughnutz” Palansky.

On July 6, 2008 Polaris completed a come-from-behind victory by posting a decisive win against Matt Hawrilenko and IJay Palansky. Polaris won both sides of the duplicate match to win convincingly.

The results are here

Match Player.........Amount Won Player........ Amount Won Difference Result
Live 1 Nick Grudzien -$42000..... Kyle Hendon... +$37000 ...-$5000... Draw
Live 2 Rich McRoberts +$89500.... Victor Acosta. -$39500 ..+$50000... Humans Win
Live 3 Mark Newhouse +$251500.... IJay Palansky. -$307500 .-$56000... Polaris Wins
Live 4 Matt Hawrilenko -$60500... IJay Palansky. -$29000 ..-$89500... Polaris Wins

It was just in April 2008 that a computer started to become competitive with humans in 9X9 Go.

Checkers (8X8) was weakly solved April 2007 by the University of Alberta They are the same group that built the Polaris poker bot

Eetimes reports, the University of Alberta group said it expects to be asked for rematches by the vanquished pros as well as by other poker experts who will claim the win by Polaris was a fluke. “Even after Deep Blue beat Kasparov, there were still some skeptics, and I think the same is true here,” said Bowling. “Over the next year or so there are going to have to be several rematches before everyone is convinced that humans have been surpassed by machines in poker.”

Bowling’s group plans to expand Polaris beyond its current limitations, enabling it to play more complicated poker games than its current heads-up, hold-em version. They also plan to expand efforts to apply the poker-playing algorithms to useful applications.

“The techniques we are devising have broad applications outside of poker,” said Bowling. “For instance, wireless sensor networks are exploring one of our poker-like algorithms to lay out sensors in buildings in a way that yields better understanding of how heat flow patterns affect efficiency.”

One algorithm, called counter-factual regret, monitored the outcome of hands lost by Polaris and what could have been done to change the outcome. Polaris could then watch for similar circumstances and adjust more effectively.

Other press coverage of the human computer poker matches

Game complexity is described here

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