Human Pros Dominating Poker AI
Carnegie Mellon's poker AI was down 626,892 chips against its human competitors at the halfway point. Can the super computer turn things around?
At the halfway point of the “Brains Vs. Artificial Intelligence” poker competition between software developed at Carnegie Mellon University and four of the world’s best players, the nod unquestionably goes to the humans.
The CMU computer program, Claudico, is playing a total of 80,000 hands of Heads-Up No-limit Texas Hold’em against Doug Polk, Dong Kim, Bjorn Li and Jason Les. And after 42,100 hands, the humans had a cumulative lead of 626,892 chips.
Three of the four players also held individual leads against Claudico. Though much could change in the week remaining, a lead of around 600,000 chips is considered statistically significant.
The team behind Claudico suggests, however, that it’s learning to adapt to the humans’ style of play.
“Claudico performs real-time reasoning while playing a hand and improves its strategy during the match by continuously computing,” says Tuomas Sandholm, a professor of computer science at Carnegie Mellon who has led development of Claudico. “I know many people are rooting for the humans, but I’m still hopeful that Claudico will give them a run for their money.”
“This is quite possibly the highest-quality poker ever seen,” Sandholm continued. “Furthermore, both Claudico and the humans are improving their game throughout the event. Both sides are already playing stronger than they were at the start. I have been extremely impressed by the pros’ ability to improve and adjust their game no matter what curve balls Claudico has thrown at them.”
If Claudico pulls out the win, it would be a massive achievement for AI. Heads-up no-limit Texas Hold’em is one of the hardest games for a computer to win because it involves mind-boggling amounts of possible decisions, not all of them immediately logical.
“You could use the same basic framework to do robust decision making like trying to come up with insulin and glucose monitoring plans [for diabetes patients],” says Neil Burch, a computer scientist at the University of Alberta who helped design a poker-playing AI earlier this year. “You get regular snapshots of glucose levels, and you have to decide how much insulin you should take, and how often.”