Forming coalitions in the face of uncertain rewards
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Coalition, cryptography, and stability: mechanisms for coalition formation in task oriented domains
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Emergent cooperative goal-satisfaction in large-scale automated-agent systems
Artificial Intelligence
Coalition structure generation with worst case guarantees
Artificial Intelligence
Coalition formation with uncertain heterogeneous information
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Fuzzy kernel-stable coalitions between rational agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Coalition Formation for Large-Scale Electronic Markets
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Adaptive load balancing: a study in multi-agent learning
Journal of Artificial Intelligence Research
Coalition formation among bounded rational agents
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Deriving multi-agent coordination through filtering strategies
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Methods for task allocation via agent coalition formation
Artificial Intelligence
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Coalition formation research in the last decade has produced an array of coalition formation mechanisms. Although these address a variety of environments and settings, they are usually inadequate for practical applications. The major limitations of the proposed mechanisms that render them inapplicable are a high computational complexity, and unrealistic assumptions regarding the availability of information. In this article we present two recent coalition formation mechanisms that attempt to overcome these limitations. One of the mechanisms introduces a very low complexity, allowing scaling to thousands of agents, and the other mechanism does not assume complete information. Rather, it assumes private, subjective and inaccurate valuation of coalitions. These two mechanisms do not solve all of the problems present in the field, however they point at promising directions that might lead to fully applicable solutions in future research.