Learning of symbiotic relations among agents by using neural networks

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

Learning of symbiotic relations among agents by using neural networks

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

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

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 language	English
Pages	583-588
Number of pages	6
Publication status	Published - 2002 Jan 1
Externally published	Yes
Event	2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States Duration: 2002 May 12 → 2002 May 17

Conference

Conference	2002 International Joint Conference on Neural Networks (IJCNN '02)
Country/Territory	United States
City	Honolulu, HI
Period	02/5/12 → 02/5/17

ASJC Scopus subject areas

Software
Artificial Intelligence

Cite this

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year = "2002",

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AU - Hirasawa, Kotaro

AU - Yoshida, Hidemasa

AU - Nakanishi, Katsushige

AU - Hu, Jinglu

AU - Murata, Junichi

PY - 2002/1/1

Y1 - 2002/1/1

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AB - 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.

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M3 - Paper

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T2 - 2002 International Joint Conference on Neural Networks (IJCNN '02)

Y2 - 12 May 2002 through 17 May 2002

ER -

Learning of symbiotic relations among agents by using neural networks

Abstract

Conference

ASJC Scopus subject areas

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