Morality algorithm lets machines cooperate and compromise better than humans
Over the past year, it's become pretty clear that machines can now beat us in many straightforward zero-sum games. A new study from an international team of computer scientists set out to develop a new type of game-playing algorithm – one that can play games that rely on traits like cooperation and compromise – and the researchers have found that machines can already deploy those characteristics better than humans.
Chess, Go and Poker are all adversarial games where two or more players are in conflict with each other. Games such as these offer clear milestones to gauge the progress of AI development, allowing humans to be pitted against computers with a tangible winner. But many real-world scenarios that AI will ultimately operate in require more complex, cooperative long term relationships between humans and machines.
"The end goal is that we understand the mathematics behind cooperation with people and what attributes artificial intelligence needs to develop social skills," says lead author on the new study Jacob Crandall. "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."
The team created an algorithm called S# and tested its performance across a variety of two-player games, either in machine-machine, human-machine or human-human interactions. The games selected, including Prisoner's Dilemma and Shapley's Game, all required different levels of cooperation or compromise for a player to achieve a high payoff. The results were fascinating, showing that in most cases the machine outperformed humans in the games.
"Two humans, if they were honest with each other and loyal, would have done as well as two machines," says Crandall. "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."
An interesting technique incorporated into the algorithm was the machine's ability to engage in what the researchers called "cheap talk." These were phrases that the machine deployed either in response to a cooperative gesture ("Sweet. We are getting rich!"), or as a reaction to another participant lying or cheating ("You will pay for that!"). When the machines deployed "cheap talk," the human players were generally unable to pick they were playing a machine, and in most cases the comments doubled the amount of cooperation.
The researchers suggest these findings could lay the foundation for better autonomous machines in the future as technologies like driverless cars require machines to interact with both humans and other machines that often don't share the same goals.
"In society, relationships break down all the time," says Crandall. "People that were friends for years all of a sudden become enemies. Because the machine is often actually better at reaching these compromises than we are, it can potentially teach us how to do this better."
The study was published in the journal Nature Communications.
Source: Brigham Young University