Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
A behavioral multi-agent model for road traffic simulation
Engineering Applications of Artificial Intelligence
Learning of coordination: exploiting sparse interactions in multiagent systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
On finding graph clusterings with maximum modularity
WG'07 Proceedings of the 33rd international conference on Graph-theoretic concepts in computer science
Formal specification of holonic multi-agent systems framework
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Computer Science Review
Holonic multi-agent system for traffic signals control
Engineering Applications of Artificial Intelligence
Hierarchical control of traffic signals using Q-learning with tile coding
Applied Intelligence
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A multi-agent system consists of a group of interacting autonomous agents. The key problem in such a system is coordination and cooperation, i.e. how to ensure that individual decisions of the agents result in jointly optimal decisions for the overall system. This problem becomes even more serious when the number of the agents is large. Holonic model is an effective method to manage large scale problems. In holonic approaches, the formation of the initial holons is very critical and has a great influence on their performance and effectiveness. In this paper, we use a graph based modelling approach to group a population of agents with a greedy method, driven by a very simple and effective quality measure. The proposed method is evaluated by applying it to an urban traffic problem as a case study and it is shown the proposed method produces better results.