How and why we make thousands of decisions every day has long proven to be a popular area of research and commentary.
"Predictably Irrational: The Hidden Forces That Shape Our Decisions," by Dan Ariely; "Nudge: Improving Decisions about Health, Wealth and Happiness," by Richard Thaler and Cass Sunstein; and "Simply Rational: Decision Making in the Real World," by Gerd Gigerenzer, are just a few of the scores of books analyzing the mechanics of decision-making that appear on current best-seller lists.
A team of researchers at the Princeton Neuroscience Institute has now joined the discussion with a paper examining the decision-making process when it comes to machine learning. They say they have found an approach that improves upon the commonly applied single-agent process.
In a paper published July 3 in Proceedings of the National Academy of Sciences, researchers outlined a study comparing reinforcement learning approaches used in single AI agent and modular multi-AI agent systems.
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