How AI Wins Friends and Influences People in Repeated Games With Cheap Talk


  • Mayada Oudah Masdar Institute
  • Talal Rahwan Khalifa University of Science and Technology
  • Tawna Crandall Brigham Young University
  • Jacob Crandall Brigham Young University



Repeated games, Human-machine collaboration


Research has shown that a person's financial success is more dependent on the ability to deal with people than on professional knowledge. Sage advice, such as "if you can't say something nice, don't say anything at all" and principles articulated in Carnegie's classic "How to Win Friends and Influence People," offer trusted rules-of-thumb for how people can successfully deal with each other. However, alternative philosophies for dealing with people have also emerged. The success of an AI system is likewise contingent on its ability to win friends and influence people. In this paper, we study how AI systems should be designed to win friends and influence people in repeated games with cheap talk (RGCTs). We create several algorithms for playing RGCTs by combining existing behavioral strategies (what the AI does) with signaling strategies (what the AI says) derived from several competing philosophies. Via user study, we evaluate these algorithms in four RGCTs. Our results suggest sufficient properties for AIs to win friends and influence people in RGCTs.




How to Cite

Oudah, M., Rahwan, T., Crandall, T., & Crandall, J. (2018). How AI Wins Friends and Influences People in Repeated Games With Cheap Talk. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).



AAAI Technical Track: Human-AI Collaboration