Abstract
Network robustness has been the key metric in the analysis of secure distributed consensus algorithms for multi-agent systems (MASs). However, it is proved that determining the network robustness of a MASs with large nodes is NP-hard. In this paper, we try to apply machine learning method to determine the robustness of MASs. We use neural network (NN) that consists of Multilayer Perceptions (MLPs) to learn the representation of multi-agent networks and use softmax as our classifiers. We compare our method with a traditional CNN-based approach on a graph-structured dataset. It is shown that with the help of machine learning method, determining robustness can be possible for MASs with large nodes.
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Acknowledgment
This work was supported by the cyberspace security Major Program in National Key Research and Development Plan of China under grant 2016YFB0800201, Natural Science Foundation of China under grants 61572165 and 61702150, State Key Program of Zhejiang Province Natural Science Foundation of China under grant LZ15F020003, Key Research and Development Plan Project of Zhejiang Province under grants 2017C01062 and 2017C01065, Public Research Project of Zhejiang Province under grant LGG18F020015, and Scientific Research fund of Zhejiang Provincial Education Department under grant Y201737924.
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Wang, G., Xu, M., Wu, Y., Zheng, N., Xu, J., Qiao, T. (2018). Using Machine Learning for Determining Network Robustness of Multi-Agent Systems Under Attacks. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_56
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DOI: https://doi.org/10.1007/978-3-319-97310-4_56
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