Learning of symbiotic relations among agents by using neural networks

Kotaro Hirasawa, Hidemasa Yoshida, Katsushige Nakanishi, Jinglu Hu, Junichi Murata

Research output: Contribution to conferencePaper

Abstract

Symbiotic relation among agents is regarded as one of the most basic relations in the complex systems. In this paper, a method for constructing the required symbiotic relations among the agents is proposed, where the agent is made up of a layered neural network and its parameters are trained in order to realize the required symbiotic relations. From simulations of the ecosystems, whose agent corresponds to the species, it has been clarified that the proposed method can give an ecosystem model with more flexible and more powerful representation abilities than the conventional Lotka-Volterra model.

Original languageEnglish
Pages583-588
Number of pages6
Publication statusPublished - 2002 Jan 1
Externally publishedYes
Event2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States
Duration: 2002 May 122002 May 17

Conference

Conference2002 International Joint Conference on Neural Networks (IJCNN '02)
CountryUnited States
CityHonolulu, HI
Period02/5/1202/5/17

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Learning of symbiotic relations among agents by using neural networks'. Together they form a unique fingerprint.

  • Cite this

    Hirasawa, K., Yoshida, H., Nakanishi, K., Hu, J., & Murata, J. (2002). Learning of symbiotic relations among agents by using neural networks. 583-588. Paper presented at 2002 International Joint Conference on Neural Networks (IJCNN '02), Honolulu, HI, United States.