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Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
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Geography-informed energy conservation for Ad Hoc routing
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Resource Allocation in the Grid Using Reinforcement Learning
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Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
An integrated token-based algorithm for scalable coordination
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
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Self-organization in multi-agent systems
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Certified reputation: how an agent can trust a stranger
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Multiagent reinforcement learning and self-organization in a network of agents
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ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Reinforcement learning: a survey
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Role evolution in Open Multi-Agent Systems as an information source for trust
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Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Decentralized approaches for self-adaptation in agent organizations
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
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Expert Systems with Applications: An International Journal
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Self-organization provides a suitable paradigm for developing self-managed complex computing systems, e.g., decision support systems. Towards this end, in this paper, a composite self-organization mechanism in an agent network is proposed. To intuitively elucidate this mechanism, a task allocation environment is simulated. Based on self-organization principles, this mechanism enables agents to dynamically adapt relations with other agents, i.e., to change the underlying network structure, so as to achieve efficient task allocation. The proposed mechanism utilizes a trust model to assist agents in reasoning with whom to adapt relations and employs a multi-agent Q-learning algorithm for agents to learn how to adapt relations. Moreover, in this mechanism, it is considered that the agents are connected by weighted relations, instead of crisp relations. The proposed mechanism is evaluated through a comparison with a centralized mechanism and the K-Adapt mechanism in both closed and open agent networks. Experimental results demonstrate the adequate performance of the proposed mechanism in terms of the entire network profit and time consumption. Additionally, a potential application scenario of this mechanism is also given, which exhibits the potential applicability of this mechanism in some real world cases.