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
fDate :
6/24/1905 12:00:00 AM
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;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007527
