“With our results we’ve been able to show that we can solve … enormous problems on a much larger scale than has been done before,” Bowling said. Solving a game as complex as heads-up limit Texas hold ’em could mean a breakthrough in our conception of how big is too big. But the bigger a problem gets, the less certain people are that they can trust it to an algorithm. Algorithms are already used to optimize solutions in numerous areas, like elevator control and security (for example, air marshal scheduling and coast guard patrolling). Bowling says the findings in this new research are especially valuable because they give us a hint at the scale of problems AI can solve. The goal of the hold ’em research is to use poker puzzles as experimental stand-ins for real-world problems. “It’s easy to make many variations of a game, which is important in order to test AI properly: To be generally intelligent, you must be good at not only one task, but many tasks,” Togelius said. This is related to the idea that algorithms can’t function if they face overwhelming uncertainty. Togelius notes that games are valuable for AI research because they can be iterative for testing.
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