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Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks Research Article

Hanqi DAI, Weining LU, Xianglong LI, Jun YANG, Deshan MENG, Yanze LIU, Bin LIANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7, Pages 1069-1076 doi: 10.1631/FITEE.2100597

Abstract: is one of the critical problems in the field of gaming. This work focuses on when each agent has only a local observation range and local communication. We propose a novel architecture that combines a graph neural network with a 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 (i.e., Graph SAmple and aggreGatE), which is the first time this method has been used to deal with the 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.

Keywords： Multi-agent system Cooperative planning GraphSAGE Task-oriented knowledge fusion