Title :
Consensus based on learning game theory
Author :
Zhongjie Lin ; Liu, Hugh H. T.
Author_Institution :
Univ. of Toronto Inst. for Aerosp. Studies, Toronto, ON, Canada
Abstract :
In this paper, a systematic distributed optimization approach is proposed based on a fictitious play concept. The convergence of the algorithm is proven under the game theory framework. The result is equivalent to a consensus problem. It introduces a novel perspective to study the consensus problem. Such an equivalence is illustrated by numerical cases.
Keywords :
game theory; learning (artificial intelligence); optimisation; consensus; distributed optimization; fictitious play concept; learning game theory; Algorithm design and analysis; Convergence; Equations; Game theory; Games; Mathematical model; Optimization;
Conference_Titel :
Guidance, Navigation and Control Conference (CGNCC), 2014 IEEE Chinese
Conference_Location :
Yantai
Print_ISBN :
978-1-4799-4700-3
DOI :
10.1109/CGNCC.2014.7007464
