Coordination techniques for distributed artificial intelligence
Foundations of distributed artificial intelligence
T&Aelig;MS: a framework for environment centered analysis and design of coordination mechanisms
Foundations of distributed artificial intelligence
General principles of learning-based multi-agent systems
Proceedings of the third annual conference on Autonomous Agents
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Distributed constraint satisfaction: foundations of cooperation in multi-agent systems
Asynchronous Weak-commitment Search for Solving Distributed Constraint Satisfaction Problems
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
Distributed optimization and flight control using collectives
Distributed optimization and flight control using collectives
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
A probability collectives approach with a feasibility-based rule for constrained optimization
Applied Computational Intelligence and Soft Computing
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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.