Coalition formation with uncertain heterogeneous information
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Organization-Based Cooperative Coalition Formation
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Research Directions for Service-Oriented Multiagent Systems
IEEE Internet Computing
Coalition formation mechanism in multi-agent systems based on genetic algorithms
Applied Soft Computing
Sequential decision making in repeated coalition formation under uncertainty
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Methods for task allocation via agent coalition formation
Artificial Intelligence
Coalition Formation Strategies for Self-Interested Agents in Task Oriented Domains
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Efficient Team Formation Based on Learning and Reorganization and Influence of Communication Delay
CIT '11 Proceedings of the 2011 IEEE 11th International Conference on Computer and Information Technology
Intelligent Decision Technologies
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We propose a learning method for efficient team formation by self-interested agents in task oriented domains. Service requests on computer networks have recently been rapidly increasing. To improve the performance of such systems, issues with effective team formation to do tasks has attracted our interest. The main feature of the proposed method is learning from two-sided viewpoints, i.e., team leaders who have the initiative to form teams or team members who work in one of the teams that are solicited. For this purpose, we introduce three parameters to agents so that they can select their roles of being a leader or a member, then an agent can anticipate which other agents should be selected as team members and which team it should join. Our experiments demonstrated that the numbers of tasks executed by successfully generated teams increased by approximately 17% compared with a conventional method.