A Detailed Analysis of a Multi-agent Diverse Team

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.

[1]  R. McKelvey,et al.  Quantal Response Equilibria for Normal Form Games , 1995 .

[2]  Christian Guttmann Making Allocations Collectively: Iterative Group Decision Making under Uncertainty , 2008, MATES.

[3]  Stefano Nolfi,et al.  Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines , 2000 .

[4]  Zongmin Ma,et al.  Computers and Games , 2008, Lecture Notes in Computer Science.

[5]  Richard James,et al.  Swarm intelligence in humans: diversity can trump ability , 2011, Animal Behaviour.

[6]  Felix A. Fischer,et al.  An integrated framework for adaptive reasoning about conversation patterns , 2005, AAMAS '05.

[7]  Nicolas de Condorcet Essai Sur L'Application de L'Analyse a la Probabilite Des Decisions Rendues a la Pluralite Des Voix , 2009 .

[8]  Leandro Soriano Marcolino,et al.  Multi-Agent Team Formation: Diversity Beats Strength? , 2013, IJCAI.

[9]  Olivier Teytaud,et al.  Modification of UCT with Patterns in Monte-Carlo Go , 2006 .

[10]  Takeshi Ito,et al.  Consultation Algorithm for Computer Shogi: Move Decisions by Majority , 2010, Computers and Games.

[11]  David West,et al.  Diversity of ability and cognitive style for group decision processes , 2009, Inf. Sci..

[12]  Phil Husbands,et al.  Evolutionary robotics , 2014, Evolutionary Intelligence.

[13]  L. Jeppesen,et al.  The Value of Openness in Scientific Problem Solving , 2007 .

[14]  Marie desJardins,et al.  Agent-organized networks for dynamic team formation , 2005, AAMAS '05.

[15]  Marco LiCalzi,et al.  The Power of Diversity Over Large Solution Spaces , 2011, Manag. Sci..

[16]  Vincent Conitzer,et al.  Common Voting Rules as Maximum Likelihood Estimators , 2005, UAI.

[17]  E. Hellinger,et al.  Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. , 1909 .

[18]  Michal Pechoucek,et al.  Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems , 2005, AAMAS 2005.

[19]  Vincent Conitzer,et al.  Computational voting theory: game-theoretic and combinatorial aspects , 2011 .

[20]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[21]  Manuela M. Veloso,et al.  Modeling and learning synergy for team formation with heterogeneous agents , 2012, AAMAS.

[22]  Thomas R. Ioerger,et al.  A Quantitative Model of Capabilities in Multi-Agent Systems , 2003, IC-AI.

[23]  Konstantinos V. Katsikopoulos,et al.  When Does Diversity Trump Ability (and Vice Versa) in Group Decision Making? A Simulation Study , 2012, PloS one.

[24]  H. Young Optimal Voting Rules , 1995 .

[25]  Noa Agmon,et al.  Leading ad hoc agents in joint action settings with multiple teammates , 2012, AAMAS.

[26]  Yann Braouezec,et al.  Committee, Expert Advice, and the Weighted Majority Algorithm: An Application to the Pricing Decision of a Monopolist , 2010 .

[27]  David Stuart Robertson,et al.  Enacting the Distributed Business Workflows Using BPEL4WS on the Multi-agent Platform , 2005, MATES.

[28]  Lu Hong,et al.  Groups of diverse problem solvers can outperform groups of high-ability problem solvers. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Sarvapali D. Ramchurn,et al.  Competing with Humans at Fantasy Football: Team Formation in Large Partially-Observable Domains , 2012, AAAI.

[30]  C. List,et al.  Epistemic democracy : generalizing the Condorcet jury theorem , 2001 .