LOTTO: group formation by overhearing in large teams
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A WSN Coalition Formation Algorithm Based on Ant Colony with Dual-Negative Feedback
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Team Formation Strategies in a Dynamic Large-Scale Environment
Massively Multi-Agent Technology
Combining Job and Team Selection Heuristics
Coordination, Organizations, Institutions and Norms in Agent Systems IV
Coalition structure generation in multi-agent systems with mixed externalities
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A distributed branch-and-bound algorithm for computing optimal coalition structures
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
An investigation into the use of group dynamics for solving social dilemmas
MABS'04 Proceedings of the 2004 international conference on Multi-Agent and Multi-Agent-Based Simulation
A pruning-based algorithm for computing optimal coalition structures in linear production domains
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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In a multiagent system where each agent has only an incomplete view of the world, optimal coalition formation is difficult. Coupling that with real-time and resource constraints often makes the rationalization process infeasible or costly. We propose a coalition formation approach that identifies and builds sub-optimal yet satisficing coalitions among agents to solve a problem detected in the environment. All agents are peers and autonomous. Each is motivated to conserve its own resources while cooperating with other agents to achieve a global task or resource allocation goal. The (initiating) agent-that detects a problem-hastily forms an initial coalition by selecting neighboring agents that it considers to have high potential utilities, based on the capability of each neighbor and its respective inter-agent relationships. The initiating agent next finalizes the coalition via multiple concurrent 1-to-1 negotiations with only neighbors of high potential utility, during which constraints and commitments are exchanged in an argumentation setting.