Price wars and niche discovery in an information economy
Proceedings of the 2nd ACM conference on Electronic commerce
Congregating and market formation
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Strategies for Discovering Coordination Needs in MultiAgent Systems
Selected papers from the UKMAS Workshop on Foundations and Applications of Multi-Agent Systems
Coalition formation through motivation and trust
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Social navigation: modeling, simulation, and experimentation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A survey of multi-agent organizational paradigms
The Knowledge Engineering Review
A flexible and reasonable mechanism for self-interested agent team forming
Multiagent and Grid Systems - Negotiation and Scheduling Mechanisms for Multiagent Systems
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
Dynamic team forming in self-interested multi-agent systems
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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We present congregating both as a metaphor for describing and modeling multiagent systems (MAS) and as a means for reducing coordination costs. We show how congregations can be used to explain and predict the behavior of self-interested agents that are searching for other agents to interact with. This framework is integrated with Vidal and Durfee's CLRI framework [11] for evaluating learning within MAS. We provide experimental and analytical results, which describe how the difficulty of the congregating problem increases exponentially with the number of agents, and present a solution to this in the form of labelers, which are agents that assign a description to a congregation, thereby reducing agents' search problem.