Artificial cognitive BP-CT ant routing algorithm

Author

Jing, Xu ; Liu, Chunyu ; Sun, Xiaobo

Author_Institution

Dept. of Autom., Comput. & Control Coll., Harbin Univ. of Sci. & Technol., China

Volume

2

fYear

2005

fDate

31 July-4 Aug. 2005

Firstpage

1098

Abstract

This paper analyses the primary features of computing intelligence in the circumstance of multi-agents modeling, and the artificial cognitive methods with computing intelligent agents, and the artificial cognitive features in reinforcement learning and the Q-routing algorithm which is a kind of reinforcement learning in the domain of intelligent network. At the same time, aiming at the problem in AntNet routing algorithm, this paper introduces BP-CT ant routing algorithm and simulates the algorithm on OMNeT++ software platform, then proves the availability of the algorithm. Considering the global planning and optimal control theory, BP-CT ant routing algorithm has some potential aspects of intelligent control, and shows good QoS performance.

Keywords

biology computing; cognitive systems; learning (artificial intelligence); multi-agent systems; OMNeT++ software platform; ant routing algorithm; artificial cognitive methods; global planning; multi-agents modeling; optimal control theory; reinforcement learning; Algorithm design and analysis; Artificial intelligence; Competitive intelligence; Computational modeling; Computer networks; Intelligent agent; Intelligent networks; Learning; Routing; Software algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on

Print_ISBN

0-7803-9048-2

Type

conf

DOI

10.1109/IJCNN.2005.1556006

Filename

1556006