A Detailed Analysis of a Multi-agent Diverse Team


Leandro Soriano Marcolino, Chao Zhang, Albert Xin Jiang, and Milind Tambe. 2014. “A Detailed Analysis of a Multi-agent Diverse Team .” In Coordination, Organizations, Institutions and Norms in Agent Systems IX. Springer-Verlag Lecture Notes in AI, edited by T. Balke, A. Chopra, F. Dignum, and B. van Riemsdijk.


In an open system we can have many different kinds of agents. However, it is a challenge to decide which agents to pick when forming multi-agent teams. In some scenarios, agents coordinate by voting continuously. When forming such teams, should we focus on the diversity of the team or on the strength of each member? Can a team of diverse (and weak) agents outperform a uniform team of strong agents? We propose a new model to address these questions. Our key contributions include: (i) we show that a diverse team can overcome a uniform team and we give the necessary conditions for it to happen; (ii) we present optimal voting rules for a diverse team; (iii) we perform synthetic experiments that demonstrate that both diversity and strength contribute to the performance of a team; (iv) we show experiments that demonstrate the usefulness of our model in one of the most difficult challenges for Artificial Intelligence: Computer Go.
See also: 2014