Condor-G: A Computation Management Agent for Multi-Institutional Grids
Cluster Computing
Be Patient and Tolerate Imprecision: How Autonomous Agents can Coordinate Effectively
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
The Bases of Effective Coordination in Decentralized Multi-Agent Systems
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
Resource allocation games with changing resource capacities
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Towards self-organising agent-based resource allocation in a multi-server environment
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Multiagent reinforcement learning and self-organization in a network of agents
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Distributed task allocation in social networks
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Methods for task allocation via agent coalition formation
Artificial Intelligence
Engineering self-organizing referral networks for trustworthy service selection
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multiagent task allocation in social networks
Autonomous Agents and Multi-Agent Systems
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Distributed resource allocation in multiagent systems is hard to solve. Since the allocation will be done distributively, agents are not aware of others that use the resources that they need and in what quantity. That is, because the agents do not have access to the entire list of allocations, they can attempt to use resources that are not available. One naive approach is to allow agents to try different allocations repeatedly, so that they can eventually an effective allocation can emerge. However, such a technique is difficult to succeed when the resources are scarce but the number of agents is high. An effective solution to the problem has to allow agents to self-organize intelligently rather than randomly. Accordingly, this paper proposes a communication scheme, where agents are allowed to exchange a small part of their prior knowledge with a few of the agents that they know. We study our proposed approach in relation to existing approaches in the literature and show the positive effects of communication on better resource allocation, especially when the resources are scarce and the agents have a variety of choices for allocation.