If humans have reached the point at which their computer opponents are just too good for them, labs like the ones that Sandholm and Bowling run are facing almost the opposite problem. Head-to-head matches against professionals are one thing. But there’s no clear path to turn Libratus and DeepStack into players that could be confident of beating a group of flawed humans. That’s because the equilibrium strategy that the AIs use fall apart in multiplayer games, when the point isn't to play perfectly but to identify and exploit the shortcomings in other people's games.
Several years ago Bowling did an experiment where three bots played against one another. Two of them used his labs’s closest approximation to perfect play; the third one was programmed to raise recklessly. At the end of the game, the dumbest bot lost a small amount of money. One of the perfect players won big, but the other one lost its shirt.
“That’s really the hard part. How do you reason about these games if you know you’re going to sit down with human players or other programs that aren’t very good?” said Bowling. “You’re going to have to be prepared for that.”
This article was provided by Bloomberg News.