Machines Learn To Cooperate With Human Partners, Who Often Cheat or Become Disloyal

MedicalResearch.com Interview with:

Jacob Crandall PhD Associate Professsor, Computer Science Brigham Young University 

Dr. Jacob Crandall

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.

MedicalResearch.com: What are the main findings?

Response: Our studies showed that, when machines followed the algorithm S#, human-machine partnerships and human-human partnerships produced similar levels of mutual cooperation on average, while machine-machine partnerships had much higher levels of mutual cooperation.  Additionally, S#’s ability to generate and respond to signals (cheap talk) at levels conducive to human understanding was critical to developing cooperative relationships with people. 

MedicalResearch.com: What should readers take away from your report?

 Response: Our studies revealed important differences between humans and S# (the machine).  For example, more than half of human participants in our studies did not follow through with a verbal commitment they made to their partner at least once.  On the other hand, the machine was programmed to be honest.  Furthermore, while the machine typically learned to be loyal to its partner once a pattern of mutual cooperation emerged, people often defected against their partner after establishing a pattern of mutual cooperation.  Our analysis indicates that had our our human participants followed the machine’s example with respect to honesty and loyalty, human-human partnerships would have been as successful as machine-machine partnerships. 

MedicalResearch.com: What recommendations do you have for future research as a result of this work?

Response: The success of the algorithm in forging cooperative relationships with people suggests that artificial intelligence may be able to help improve our abilities to cooperate with each other.  While humans are often good at cooperating, human relationships still frequently break down.  People that were friends for years suddenly become enemies.  Additionally, many potential human relationships never develop because of our inabilities to resolve perceived differences.  We hope that future work can continue to address how artificial intelligence can help people get along with each other. 

Citations:

Cooperating with Machines

Jacob W. CrandallMayada OudahTennomFatimah Ishowo-OlokoSherief AbdallahJean-François BonnefonManuel CebrianAzim ShariffMichael A. GoodrichIyad Rahwan, last revised 16 Jan 2018 (this version, v4))

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