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Multiagent systems
Dynamic Load Balancing on Web-Server Systems
IEEE Internet Computing
An Organization-Related Information Maintenance Component
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Exploiting as hierarchy for scalable route selection in multi-homed stub networks
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
lbnamed: A Load Balancing Name Server in Perl
LISA '95 Proceedings of the 9th USENIX conference on System administration
Agent-organized networks for dynamic team formation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Adaptive load balancing: a study in multi-agent learning
Journal of Artificial Intelligence Research
Multi-Agent Systems Performance by Adaptive/Non-Adaptive Agent Selection
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Adaptive manager-side control policy in contract net protocol for massively multi-agent systems
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Controling Contract Net Protocol by Local Observation for Large-Scale Multi-Agent Systems
CIA '08 Proceedings of the 12th international workshop on Cooperative Information Agents XII
Adaptive agent selection in large-scale multi-agent systems
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Fluctuated peer selection policy and its performance in large-scale multi-agent systems
Web Intelligence and Agent Systems
Automated assembly of Internet-scale software systems involving autonomous agents
Journal of Systems and Software
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In order to achieve efficient progress in activities such as e-commerce and e-transactions in an open environment like the Internet, an agent must choose appropriate partner agents for collaboration. However, agents have no global information about the whole multi-agent system (MAS) and the state of the Internet; therefore, they must select the appropriate partners based on local knowledge and local observations. In this paper, using a multi-agent simulation, we discuss how total MAS performances are affected by local decisions when agents select partners to collaborate with. We also investigate how MAS performances change and how network structures between agents shift according to the progress of agents' local learning and observations. We then discuss the relationship between task load and agent network structure. This relates to establishing the optimum time when agents should learn about appropriate partners in an actual environment.