A composite self-organisation mechanism in an agent network

Authors:
Dayong Ye;Minjie Zhang;Quan Bai
Affiliations:
University of Wollongong, Wollongong, Australia;University of Wollongong, Wollongong, Australia;Auckland University of Technology, Auckland, New Zealand
Venue:
WISE'11 Proceedings of the 12th international conference on Web information system engineering
Year:
2011

Citing 7
Cited 0

Principles for Dynamic Multi-agent Organizations

Proceedings of the 5th Pacific Rim International Workshop on Multi Agents: Intelligent Agents and Multi-Agent Systems
Self-organization in multi-agent systems

The Knowledge Engineering Review
Dynamic analysis of multiagent Q-learning with ε-greedy exploration

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Self-organising agent organisations

Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Reinforcement learning: a survey

Journal of Artificial Intelligence Research
Role evolution in Open Multi-Agent Systems as an information source for trust

Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Changing neighbours: improving tag-based cooperation

Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

Self-organisation provides a suitable paradigm for developing autonomic web-based applications, e.g., e-commerce. Towards this end, in this paper, a composite self-organisation mechanism in an agent network is proposed. Based on self-organisation principles, this mechanism enables agents to dynamically adapt relations with other agents, i.e., change the underlying network structure, to achieve efficient task allocation. The proposed mechanism integrates 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.