Research Article
Coalition formation through learning in autonomic networks
@INPROCEEDINGS{10.1109/GAMENETS.2009.5137377, author={Tao Jiang and John S. Baras}, title={Coalition formation through learning in autonomic networks}, proceedings={1st International Conference on Game Theory for Networks}, publisher={IEEE}, proceedings_a={GAMENETS}, year={2009}, month={6}, keywords={}, doi={10.1109/GAMENETS.2009.5137377} }- Tao Jiang
John S. Baras
Year: 2009
Coalition formation through learning in autonomic networks
GAMENETS
IEEE
DOI: 10.1109/GAMENETS.2009.5137377
Abstract
Autonomic networks rely on the cooperation of participating nodes for almost all their functions. However, due to resource constraints, nodes are generally selfish and try to maximize their own benefit when participating in the network. Therefore, it is important to study mechanisms, which can be used as incentives for cooperation inside the network. In this paper, the interactions among nodes are modelled as games. A node joins a coalition if it decides to cooperate with at least one node in the coalition. The dynamics of coalition formation proceed via nodes that interact strategically and adapt their behavior to the observed behavior of others. We present conditions that the coalition formed is stable in terms of Nash stability and the core of the coalitional game.