demonstration

The implementation of stigmergy in network-assisted multi-agent system

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Honggang ZhangAuthors Info & Claims

MobiCom '20: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking

Article No.: 68, Pages 1 - 2

https://doi.org/10.1145/3372224.3417318

Published: 18 September 2020 Publication History

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Abstract

Multi-agent system (MAS) needs to mobilize multiple simple agents to complete complex tasks. However, it is difficult to coherently coordinate distributed agents by means of limited local information. In this demo, we propose a decentralized collaboration method named as "stigmergy" in network-assisted MAS, by exploiting digital pheromones (DP) as an indirect medium of communication and utilizing deep reinforcement learning (DRL) on top. Correspondingly, we implement an experimental platform, where KHEPERA IV robots form targeted specific shapes in a decentralized manner. Experimental results demonstrate the effectiveness and efficiency of the proposed method. Our platform could be conveniently extended to investigate the impact of network factors (e.g., latency, data rate, etc).

References

[1]

Rongpeng Li, Zhifeng Zhao, Xing Xu, Fei Ni, and Honggang Zhang. 2020. The Collective Advantage for Advancing Communications and Intelligence. IEEE Wireless Communications (2020), 1--7.

Google Scholar

[2]

H Van Dyke Parunak, Sven A Brueckner, and John Sauter. 2004. Digital pheromones for coordination of unmanned vehicles. In International Workshop on Environments for Multi-Agent Systems. Springer, 246--263.

Google Scholar

[3]

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics. 1273--1282.

Google Scholar

[4]

Xing Xu, Zhifeng Zhao, Rongpeng Li, and Honggang Zhang. 2019. Brain-inspired stigmergy learning. IEEE Access 7 (2019), 54410--54424.

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[5]

Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Tim Harley, Timothy P. Lillicrap, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous Methods for Deep Reinforcement Learning. In Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48. JMLR.org, 1928--1937.

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Xu XLi RZhao ZZhang H(2022)Stigmergic Independent Reinforcement Learning for Multiagent CollaborationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.305641833:9(4285-4299)Online publication date: Sep-2022
https://doi.org/10.1109/TNNLS.2021.3056418

Index Terms

The implementation of stigmergy in network-assisted multi-agent system
1. Computer systems organization
2. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Cooperation and coordination
      2. Multi-agent systems

Index terms have been assigned to the content through auto-classification.

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Published In

MobiCom '20: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking

April 2020

621 pages

ISBN:9781450370851

DOI:10.1145/3372224

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 September 2020

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National Natural Science Foundation of China
Zhejiang Lab
Zhejiang Provincial Natural Science Foundation of China
National Key R&D Program of China
Zhejiang Key Research and Development Plan

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MobiCom '20

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SIGMOBILE

MobiCom '20: The 26th Annual International Conference on Mobile Computing and Networking

September 21 - 25, 2020

London, United Kingdom

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Xu XLi RZhao ZZhang H(2022)Stigmergic Independent Reinforcement Learning for Multiagent CollaborationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.305641833:9(4285-4299)Online publication date: Sep-2022
https://doi.org/10.1109/TNNLS.2021.3056418

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Synthesizing stigmergy for multi agent systems

Common understanding in a multi-agent system using ontology-guided learning

Coordination as inference in multi-agent reinforcement learning

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