Author Interviews, Technology / 21.01.2018

MedicalResearch.com Interview with: [caption id="attachment_39439" align="alignleft" width="200"]Jacob Crandall PhD Associate Professsor, Computer Science Brigham Young University  Dr. Jacob Crandall[/caption] 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.