Give a Hard Problem to a Diverse Team: Exploring Large Action Spaces


  • Leandro Soriano Marcolino University of Southern California
  • Haifeng Xu University of Southern California
  • Albert Xin Jiang University of Southern California
  • Milind Tambe University of Southern California
  • Emma Bowring University of the Pacific



Team formation, Coordination & Collaboration, Distributed AI


Recent work has shown that diverse teams can outperform a uniform team made of copies of the best agent. However, there are fundamental questions that were not asked before. When should we use diverse or uniform teams? How does the performance change as the action space or the teams get larger? Hence, we present a new model of diversity for teams, that is more general than previous models. We prove that the performance of a diverse team improves as the size of the action space gets larger. Concerning the size of the diverse team, we show that the performance converges exponentially fast to the optimal one as we increase the number of agents. We present synthetic experiments that allow us to gain further insights: even though a diverse team outperforms a uniform team when the size of the action space increases, the uniform team will eventually again play better than the diverse team for a large enough action space. We verify our predictions in a system of Go playing agents, where we show a diverse team that improves in performance as the board size increases, and eventually overcomes a uniform team.




How to Cite

Soriano Marcolino, L., Xu, H., Xin Jiang, A., Tambe, M., & Bowring, E. (2014). Give a Hard Problem to a Diverse Team: Exploring Large Action Spaces. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



AAAI Technical Track: Multiagent Systems