Distributed team formation in multi-agent systems: Stability and approximation

We consider a scenario in which leaders are required to recruit teams of followers. Each leader cannot recruit all followers, but interaction is constrained according to a bipartite network. The objective for each leader is to reach a state of local stability in which it controls a team whose size is equal to a given constraint. We focus on distributed strategies, in which agents have only local information of the network topology and propose a distributed algorithm in which leaders and followers act according to simple local rules. The performance of the algorithm is analyzed with respect to the convergence to a stable solution. Our results are as follows. For any network, the proposed algorithm is shown to converge to an approximate stable solution in polynomial time, namely the leaders quickly form teams in which the total number of additional followers required to satisfy all team size constraints is an arbitrarily small fraction of the entire population. In contrast, for general graphs there can be an exponential time gap between convergence to an approximate solution and to a stable solution.

[1]  Éva Tardos,et al.  Balanced outcomes in social exchange networks , 2008, STOC.

[2]  Alireza Tahbaz-Salehi,et al.  Consensus Over Ergodic Stationary Graph Processes , 2010, IEEE Transactions on Automatic Control.

[3]  Victor R. Lesser,et al.  Organization-based cooperative coalition formation , 2004, Proceedings. IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2004. (IAT 2004)..

[4]  EgerstedtMagnus,et al.  Controllability of Multi-Agent Systems from a Graph-Theoretic Perspective , 2009 .

[5]  Jorge Cortes,et al.  Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms , 2009 .

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

[7]  Alireza Tahbaz-Salehi,et al.  A Necessary and Sufficient Condition for Consensus Over Random Networks , 2008, IEEE Transactions on Automatic Control.

[8]  Yevgeniy Vorobeychik,et al.  Behavioral dynamics and influence in networked coloring and consensus , 2010, Proceedings of the National Academy of Sciences.

[9]  H.G. Tanner,et al.  On the controllability of nearest neighbor interconnections , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[10]  J. Stephen Judd,et al.  Behavioral experiments on biased voting in networks , 2009, Proceedings of the National Academy of Sciences.

[11]  Kevin M. Passino,et al.  Distributed Task Assignment for Mobile Agents , 2007, IEEE Transactions on Automatic Control.

[12]  Spring Berman,et al.  Optimized Stochastic Policies for Task Allocation in Swarms of Robots , 2009, IEEE Transactions on Robotics.

[13]  M. Kearns,et al.  An Experimental Study of the Coloring Problem on Human Subject Networks , 2006, Science.

[14]  Lovekesh Vig,et al.  Multi-robot coalition formation , 2006, IEEE Transactions on Robotics.

[15]  Massimo Franceschetti,et al.  A Group Membership Algorithm with a Practical Specification , 2001, IEEE Trans. Parallel Distributed Syst..

[16]  Scott Duke Kominers,et al.  Multilateral matching , 2011, EC '11.

[17]  Massimo Franceschetti,et al.  Human Matching Behavior in Social Networks: An Algorithmic Perspective , 2012, PloS one.

[18]  Luca Schenato,et al.  Decentralized task assignment in camera networks , 2010, 49th IEEE Conference on Decision and Control (CDC).

[19]  Ramamohan Paturi,et al.  Connected Coordination: Network Structure and Group Coordination , 2009 .

[20]  Predrag T. Tosic,et al.  Maximal Clique Based Distributed Group Formation for Autonomous Agent Coalitions , 2004 .

[21]  Frank Harary,et al.  Graph Theory , 2016 .

[22]  Magnus Egerstedt,et al.  Controllability of Multi-Agent Systems from a Graph-Theoretic Perspective , 2009, SIAM J. Control. Optim..

[23]  Itai Ashlagi,et al.  Matching with couples revisited , 2010, EC '11.

[24]  Andrea Montanari,et al.  The spread of innovations in social networks , 2010, Proceedings of the National Academy of Sciences.

[25]  P. Sanders,et al.  A simpler linear time 2 / 3 − ε approximation for maximum weight matching , 2004 .

[26]  LesserVictor,et al.  A survey of multi-agent organizational paradigms , 2004 .

[27]  Ramamohan Paturi,et al.  Does more connectivity help groups to solve social problems , 2011, EC '11.

[28]  Eyton,et al.  The Diffusion of Innovations in Social Networks , 2002 .

[29]  Peter Sanders,et al.  A simpler linear time 2/3-epsilon approximation for maximum weight matching , 2004, Inf. Process. Lett..

[30]  J. Kleinberg Algorithmic Game Theory: Cascading Behavior in Networks: Algorithmic and Economic Issues , 2007 .

[31]  Seif Haridi,et al.  Distributed Algorithms , 1992, Lecture Notes in Computer Science.

[32]  J.N. Tsitsiklis,et al.  Convergence Rates in Distributed Consensus and Averaging , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[33]  Richard M. Karp,et al.  A n^5/2 Algorithm for Maximum Matchings in Bipartite Graphs , 1971, SWAT.

[34]  Yashodhan Kanoria,et al.  Fast convergence of natural bargaining dynamics in exchange networks , 2010, SODA '11.

[35]  K. Cook,et al.  The Distribution of Power in Exchange Networks: Theory and Experimental Results , 1983, American Journal of Sociology.

[36]  Antonio Bicchi,et al.  Steering a Leader-Follower Team Via Linear Consensus , 2008, HSCC.

[37]  Asuman E. Ozdaglar,et al.  Constrained Consensus and Optimization in Multi-Agent Networks , 2008, IEEE Transactions on Automatic Control.

[38]  Victor R. Lesser,et al.  A survey of multi-agent organizational paradigms , 2004, The Knowledge Engineering Review.

[39]  Alvin E. Roth,et al.  Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis , 1990 .

[40]  A. Roth The Evolution of the Labor Market for Medical Interns and Residents: A Case Study in Game Theory , 1984, Journal of Political Economy.

[41]  Asuman E. Ozdaglar,et al.  Convergence rate for consensus with delays , 2010, J. Glob. Optim..

[42]  J.N. Tsitsiklis,et al.  Convergence in Multiagent Coordination, Consensus, and Flocking , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[43]  L. S. Shapley,et al.  College Admissions and the Stability of Marriage , 2013, Am. Math. Mon..