Collective-Based Multiagent Coordination: A Case Study

In this paper we evaluate Probability Collectives (PC) as a framework for the coordination of collectives of agents. PC allows for efficient multiagent coordination without the need of explicit acquaintance models. We selected Distributed Constraint Satisfaction as case study to evaluate the PC approach for the well-known 8-Queens problem. Two different architectural structures have been implemented, one centralized and one decentralized. We have also compared between the decentralized version of PC and ADOPT, the state of the art in distributed constraint satisfaction algorithms.

[1]  Makoto Yokoo,et al.  Adopt: asynchronous distributed constraint optimization with quality guarantees , 2005, Artif. Intell..

[2]  Theodore Groves,et al.  Incentives in Teams , 1973 .

[3]  Kagan Tumer.  Coordination in Large Collectives , 2004 .

[4]  Kagan Tumer,et al.  An Introduction to Collective Intelligence , 1999, ArXiv.

[5]  Sascha Ossowski.  Constraint Based Coordination of Autonomous Agents , 2001, APPIA-GULP-PRODE.

[6]  Makoto Yokoo,et al.  Distributed Constraint Satisfaction: Foundations of Cooperation in Multi-agent Systems , 2000 .

[7]  Makoto Yokoo,et al.  Distributed Constraint Satisfaction , 2000, Springer Series on Agent Technology.

[8]  Makoto Yokoo,et al.  Asynchronous Weak-commitment Search for Solving Distributed Constraint Satisfaction Problems , 1995, CP.

[9]  David H. Wolpert,et al.  Distributed Constrained Optimization with Semicoordinate Transformations , 2008, ArXiv.

[10]  Ilan Kroo,et al.  Distributed optimization and flight control using collectives , 2005 .

[11]  David H. Wolpert,et al.  Discrete, Continuous, and Constrained Optimization Using Collectives , 2004 .

[12]  David H. Wolpert,et al.  Information Theory - The Bridge Connecting Bounded Rational Game Theory and Statistical Physics , 2004, ArXiv.

[13]  Kagan Tumer,et al.  General principles of learning-based multi-agent systems , 1999, AGENTS '99.

[14]  Victor Lesser,et al.  Environment Centered Analysis and Design of Coordination Mechanisms , 1996 .

[15]  Geoffrey S. Ryder,et al.  A probability collectives approach to weighted clustering algorithms for ad hoc networks , 2005, Communications and Computer Networks.

[16]  Gerhard Weiß.  Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments , 1997, Lecture Notes in Computer Science.

[17]  Thomas Bäck,et al.  Evolutionary computation: an overview , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[18]  Leon G. Higley,et al.  Forensic Entomology: An Introduction , 2009 .

[19]  Nicholas R. Jennings,et al.  Coordination techniques for distributed artificial intelligence , 1996 .

[20]  G. Hardin.  The Tragedy of the Commons , 2009 .

[21]  Franco Zambonelli,et al.  Self-Organizing Spatial Shapes in Mobile Particles: The TOTA Approach , 2004, Engineering Self-Organising Systems.

[22]  Kagan Tumer,et al.  Optimal Payoff Functions for Members of Collectives , 2001, Adv. Complex Syst..