DocumentCode
1679184
Title
Successive adaptation of neural networks in a multi-agent model
Author
Ishibuchi, Hisao ; Seguchi, Teppei
Author_Institution
Dept. of Ind. Eng., Osaka Prefecture Univ., Japan
Volume
3
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
2454
Lastpage
2459
Abstract
This paper examines the adaptability of neural networks to gradual and sudden changes in the environment of a non-cooperative repeated market selection game. Neural networks are used as decision-making systems of agents for iterative game playing. Training data are successively generated from each round of our game by the neural networks
Keywords
game theory; learning (artificial intelligence); marketing data processing; multi-agent systems; neural nets; decision-making; learning rate; multiple agent model; neural networks; noncooperative game; repeated market selection game; Computational modeling; Costs; Decision making; Environmental economics; Game theory; Industrial engineering; Intelligent networks; Neural networks; Training data; Transportation;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007527
Filename
1007527
Link To Document