DocumentCode :
2316800
Title :
Coalition formation through learning in autonomic networks
Author :
Jiang, Tao ; Baras, John S.
Author_Institution :
Inst. for Syst. Res., Univ. of Maryland, College Park, MD, USA
fYear :
2009
fDate :
13-15 May 2009
Firstpage :
10
Lastpage :
16
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.
Keywords :
game theory; learning (artificial intelligence); telecommunication networks; Nash stability; autonomic network; coalition formation; coalitional game; learning; resource constraint; Collaborative work; Communication networks; Communication system control; Costs; Educational institutions; Game theory; Nash equilibrium; Routing; Spread spectrum communication; Stability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Game Theory for Networks, 2009. GameNets '09. International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-4176-1
Electronic_ISBN :
978-1-4244-4177-8
Type :
conf
DOI :
10.1109/GAMENETS.2009.5137377
Filename :
5137377
Link To Document :
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