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Let's make a deal: Could AI compromise better than humans?


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Computers can play a pretty mean round of chess and keep up with the best of their human counterparts in other zero-sum games. But teaching them to cooperate and compromise instead of compete?

With help from a new algorithm created by BYU computer science professors Jacob Crandall and Michael Goodrich, along with colleagues at MIT and other international universities, machine compromise and cooperation appears not just possible, but at times even more effective than among humans.

"The end goal is that we understand the mathematics behind cooperation with people and what attributes artificial intelligence needs to develop social skills," said Crandall, whose study was recently published in Nature Communications. "AI needs to be able to respond to us and articulate what it's doing. It has to be able to interact with other people."

For the study, researchers programmed machines with an algorithm called S# and ran them through a variety of two-player games to see how well they would cooperate in certain relationships. The team tested machine-machine, human-machine and human-human interactions. In most instances, machines programmed with S# outperformed humans in finding compromises that benefit both parties.

"Two humans, if they were honest with each other and loyal, would have done as well as two machines," Crandall said. "As it is, about half of the humans lied at some point. So essentially, this particular algorithm is learning that moral characteristics are good. It's programmed to not lie, and it also learns to maintain cooperation once it emerges."

Researchers further fortified the machines' ability to cooperate by programming them with a range of "cheap talk" phrases. In tests, if human participants cooperated with the machine, the machine might respond with a "Sweet. We are getting rich!" or "I accept your last proposal." If the participants tried to betray the machine or back out
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