Methods for task allocation via agent coalition formation
Artificial Intelligence
Coalition structure generation with worst case guarantees
Artificial Intelligence
A stable and efficient buyer coalition formation scheme for e-marketplaces
Proceedings of the fifth international conference on Autonomous agents
Generating Coalition Structures with Finite Bound from the Optimal Guarantees
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
The Advantages of Compromising in Coalition Formation with Incomplete Information
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Combinatorial Auctions
Multi-Agent Collaboration: A Satellite Constellation Case
Proceedings of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium
An Incremental Adaptive Organization for a Satellite Constellation
Organized Adaption in Multi-Agent Systems
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Coalition formation is an important form of interaction in multiagent systems. It enables the agents to satisfy tasks that they would otherwise be unable to perform, or would perform with a lower efficiency. The focus of our work is on real-world application domains where we have systems inhabited by rational, self-interested agents. We also assume an environment without any trusted central manager to resolve issues concerning multiple agents. For such environments, we have to determine both an optimal (utility-maximizing) coalition configuration and a stable payoff configuration, concurrently and in a distributed fashion. Solving each of these problems is known to be computationally expensive, and having to consider them together exacerbates the problem further. In this paper, we present our Progressive, Anytime, Convergent, and Time-efficient (PACT) algorithm for coalition formation to address the above concerns. We assess the stability of the resulting coalition by using a new stability concept, the relaxed core, which is a slight variation on the core. We show experimentally that our algorithm performs admirably in comparison to an optimal solution, it typically produces solutions that are relaxed-core-stable, and it scales well.