MedicalResearch.com Interview with:
Jacob Crandall PhD
Associate Professsor, Computer Science
Brigham Young University
MedicalResearch.com: What is the background for this study?
Response: As autonomous machines become increasingly prevalent in society, they must have the ability to forge cooperative relationships with people who do not share all of their preferences. Unlike the zero-sum scenarios (e.g., Checkers, Chess, Go) often addressed by artificial intelligence, cooperation does not require sheer computational power. Instead, it is facilitated by intuition, emotions, signals, cultural norms, and pre-evolved dispositions. To understand how to create machines that cooperate with people, we developed an algorithm (called S#) that combines a state-of-the-art reinforcement learning algorithm with mechanisms for signals.
We compared the performance of S# with people in a variety of repeated games.