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.