Token approach for role allocation in extreme teams: analysis and experimental evaluation

Open computational systems comprise physical entities coordinating their activities in dynamic environments. Many exciting applications require a large number of such entities to achieve team coordination in complex missions execution. To meet the fundamental challenge of role allocation in such extreme teams, we propose an algorithm called LA-DCOP, that overcomes the limitations of previous algorithms by incorporating three key ideas. First, we represent the role allocation problem as a distributed constraint optimization problem and use tokens representing roles to minimize constraint violations. Second, we use probabilistic information about the team to guide the search quickly towards good solutions. Third, we designed the algorithm to manage constrained roles. We show that LA-DCOP not only meets our requirements in extreme teams, but also compares favorably against previous role allocation algorithms. LA-DCOP has allowed an order of magnitude scale-up in extreme teams, with role allocation in a fully distributed proxy-based teams with up to 200 members.

[1]  Éva Tardos,et al.  An approximation algorithm for the generalized assignment problem , 1993, Math. Program..

[2]  Kathleen Steinhöfel,et al.  Stochastic Algorithms: Foundations and Applications , 2002, Lecture Notes in Computer Science.

[3]  Andrea Omicini,et al.  Supporting coordination in open computational systems with TuCSon , 2003, WET ICE 2003. Proceedings. Twelfth IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2003..

[4]  Franco Zambonelli,et al.  Developing adaptive and context-aware applications in dynamic network , 2003, WET ICE 2003. Proceedings. Twelfth IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2003..

[5]  Maja J. Mataric,et al.  Broadcast of Local Elibility for Multi-Target Observation , 2000, DARS.

[6]  Thomas Wagner,et al.  A key-based coordination algorithm for dynamic readiness and repair service coordination , 2003, AAMAS '03.

[7]  Takayuki Ito,et al.  Task Allocation in the RoboCup Rescue Simulation Domain: A Short Note , 2001, RoboCup.

[8]  Hiroaki Kitano,et al.  RoboCup: A Challenge Problem for AI , 1997, AI Mag..

[9]  Victor R. Lesser,et al.  Cooperative negotiation for soft real-time distributed resource allocation , 2003, AAMAS '03.

[10]  Pedro V. Sander,et al.  A scalable, distributed algorithm for efficient task allocation , 2002, AAMAS '02.

[11]  Stephen Fitzpatrick,et al.  An Experimental Assessment of a Stochastic, Anytime, Decentralized, Soft Colourer for Sparse Graphs , 2001, SAGA.

[12]  Milind Tambe,et al.  A prototype infrastructure for distributed robot-agent-person teams , 2003, AAMAS '03.

[13]  Gil Tidhar,et al.  On team knowledge and common knowledge , 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

[14]  Weixiong Zhang,et al.  Distributed breakout revisited , 2002, AAAI/IAAI.

[15]  Pragnesh Jay Modi,et al.  Distributed constraint optimization and its application to multiagent resource allocation , 2002, AAAI/IAAI.

[16]  Luke Hunsberger,et al.  A combinatorial auction for collaborative planning , 2000, Proceedings Fourth International Conference on MultiAgent Systems.

[17]  G. Tidhar,et al.  Guided Team Selection * , 1996 .