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Emergent Cooperative Goal-Satisfaction in Large Scale Automated-Agent Systems
O. Shehory, S. Kraus, and O. Yadgar
Artificial Intelligence, 1999.

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Cooperation among autonomous agents has been discussed in the DAI community for several years. Papers about cooperation, negotiation, and coalition formation, have provided a variety of approaches and several algorithms and solutions to situations wherein cooperation is possible. However, the case of cooperation in large-scale multi-agent systems (MAS) has not been thoroughly examined. Therefore, in this paper we present a framework for cooperative goal-satisfaction in large-scale environments focusing on a low complexity physics-oriented approach. The multi-agent systems with which we deal are modeled by a physics-oriented model. According to the model, MAS inherit physical properties, and therefore the evolution of the computational systems is similar to the evolution of physical systems. To enable implementation of the model, we provide a detailed algorithm to be used by a single agent within the system. The model and the algorithm are appropriate for large-scale, dynamic, Distributed Problem Solver systems, in which agents try to increase the benefits of the whole system. The complexity is very low, and in some specific cases it is proved to be optimal. The analysis and assessment of the algorithm are performed via the well-known behavior and properties of the modeling physical system.

O. Shehory, S. Kraus, and O. Yadgar, "Emergent Cooperative Goal-Satisfaction in Large Scale Automated-Agent Systems," Artificial Intelligence, 1999.

@article{Shehory_1999_2124,
   author = "Onn Shehory and Sarit Kraus and Osher Yadgar",
   title = "Emergent Cooperative Goal-Satisfaction in Large Scale Automated-Agent Systems",
   journal = "Artificial Intelligence",
   year = "1999"
}