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CLC number: TP242.6
On-line Access: 2022-07-21
Received: 2021-12-31
Revision Accepted: 2022-07-21
Crosschecked: 2022-05-23
Cited: 0
Clicked: 1666
Citations: Bibtex RefMan EndNote GB/T7714
Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks
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Hanqi DAI, Weining LU, Xianglong LI, Jun YANG, Deshan MENG, Yanze LIU, Bin LIANG. Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(7): 1069-1076.
@article{title="Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks",
author="Hanqi DAI, Weining LU, Xianglong LI, Jun YANG, Deshan MENG, Yanze LIU, Bin LIANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="7",
pages="1069-1076",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100597"
}
%0 Journal Article
%T Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks
%A Hanqi DAI
%A Weining LU
%A Xianglong LI
%A Jun YANG
%A Deshan MENG
%A Yanze LIU
%A Bin LIANG
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 7
%P 1069-1076
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100597
TY - JOUR
T1 - Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks
A1 - Hanqi DAI
A1 - Weining LU
A1 - Xianglong LI
A1 - Jun YANG
A1 - Deshan MENG
A1 - Yanze LIU
A1 - Bin LIANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 7
SP - 1069
EP - 1076
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100597
Abstract: cooperative planning is one of the critical problems in the field of multi-agent system gaming. This work focuses on cooperative planning when each agent has only a local observation range and local communication. We propose a novel cooperative planning architecture that combines a graph neural network with a task-oriented knowledge fusion sampling method. Two main contributions of this paper are based on the comparisons with previous work: (1) we realize feasible and dynamic adjacent information fusion using graphSAGE (i.e., Graph SAmple and aggreGatE), which is the first time this method has been used to deal with the cooperative planning problem, and (2) a task-oriented sampling method is proposed to aggregate the available knowledge from a particular orientation, to obtain an effective and stable training process in our model. Experimental results demonstrate the good performance of our proposed method.
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