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Decentralizing Self-organizing Maps

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AI 2021: Advances in Artificial Intelligence (AI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13151))

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Abstract

This paper presents an algorithm for a decentralized self-organizing map. With the explosion in the availability of robotics platforms, and their increasing application to multi-agent systems and robot swarms, there is a need for a new generation of machine learning algorithms that can exploit the distributed nature of sensing and processing that can be achieved using such platforms. In this paper we examine one such algorithm for decentralized pattern recognition, assuming sensors and processors are distributed across multiple agents: a decentralized self-organizing map. We examine the proposed algorithm under a range of conditions. This includes numbers of agents, communication topologies between agents and number of encounters between agents, simulating their presence in smaller or larger spaces. We demonstrate the conditions in which our decentralised self-organizing map can achieve comparable learning performance to a centralized self-organizing map on a range of synthetic datasets.

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References

  1. Rone, W., Ben-Tzvi, P.: Mapping, localization and motion planning in mobile multi-robotic systems. Robotica 31(1), 1 (2013)

    Article  Google Scholar 

  2. Saeedi, S., et al.: Multiple-robot simultaneous localization and mapping: a review. J. Field Robot. 33(1), 3–46 (2016)

    Article  MathSciNet  Google Scholar 

  3. Howe, E., Novosad, J.: Extending slam to multiple robots, March 2005

    Google Scholar 

  4. Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)

    Article  Google Scholar 

  5. Khan, M.M., Kasmarik, K., Barlow, M.: Autonomous detection of collective behaviours in swarms. Swarm Evol. Comput. 57, 100715 (2020)

    Google Scholar 

  6. Krinkin, K., Filatov, A., Filatov, A.: Modern multi-agent slam approaches survey. In: Proceedings of the XXth Conference of Open Innovations Association FRUCT (2017)

    Google Scholar 

  7. Tanner, H.G., Christodoulakis, D.K.: Decentralized cooperative control of heterogeneous vehicle groups. Robot. Auton. Syst. 55(11), 811–823 (2007)

    Article  Google Scholar 

  8. Cheng, T.M., Savkin, A.V., Javed, F.: Decentralized control of a group of mobile robots for deployment in sweep coverage. Robot. Auton. Syst. 59(7–8), 497–507 (2011)

    Article  Google Scholar 

  9. Acevedo, J.J., et al.: A decentralized algorithm for area surveillance missions using a team of aerial robots with different sensing capabilities. In: 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2014)

    Google Scholar 

  10. Jiménez, A.C., García-Díaz, V., Bolaños, S.: A decentralized framework for multi-agent robotic systems. Sensors 18(2), 417 (2018)

    Article  Google Scholar 

  11. Rizk, Y., Awad, M., Tunstel, E.W.: Cooperative heterogeneous multi-robot systems: a survey. ACM Comput. Surv. (CSUR) 52(2), 1–31 (2019)

    Article  Google Scholar 

  12. Best, G., Hollinger, G.A.: Decentralised self-organising maps for multi-robot information gathering. In: Proceeding of IEEE/RSJ IROS (2020)

    Google Scholar 

  13. Qin, J., et al.: Recent advances in consensus of multi-agent systems: a brief survey. IEEE Trans. Industr. Electron. 64(6), 4972–4983 (2016)

    Article  Google Scholar 

  14. Di Fatta, G., et al.: Fault tolerant decentralised k-means clustering for asynchronous large-scale networks. J. Parallel Distrib. Comput. 73(3), 317–329 (2013)

    Article  Google Scholar 

  15. Mashayekhi, H., et al.: GDCluster: a general decentralized clustering algorithm. IEEE Trans. Knowl. Data Eng. 27(7), 1892–1905 (2015)

    Article  Google Scholar 

  16. Hamel, L., Ott, B.: A population based convergence criterion for self-organizing maps. In: Proceedings of the International Conference on Data Mining (DMIN): The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) (2012)

    Google Scholar 

  17. Yin, H., Allinson, N.M.: On the distribution and convergence of feature space in self-organizing maps. Neural Comput. 7(6), 1178–1187 (1995)

    Article  Google Scholar 

  18. Berger, V.W., Zhou, Y.: Kolmogorov–smirnov test: Overview. Wiley statsref: Statistics reference online (2014)

    Google Scholar 

  19. Vettigli, G.: MiniSom: minimalistic and NumPy-based implementation of the Self Organizing Map (2018). https://github.com/JustGlowing/minisom/

  20. Parisi, G.I., et al.: Continual lifelong learning with neural networks: a review. Neural Netw. 113, 54–71 (2019)

    Article  Google Scholar 

  21. Ultsch, A.: Clustering with SOM: U*C. In: Workshop on Self-Organizing Maps (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Engineering and IT, UNSW Canberra, Canberra, Australia

    Md Mohiuddin Khan, Kathryn Kasmarik & Matt Garratt

Authors
  1. Md Mohiuddin Khan
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  2. Kathryn Kasmarik
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  3. Matt Garratt
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Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Guodong Long

  2. RMIT University, Melbourne, SA, Australia

    Xinghuo Yu

  3. University of Queensland, Brisbane, QLD, Australia

    Sen Wang

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Khan, M.M., Kasmarik, K., Garratt, M. (2022). Decentralizing Self-organizing Maps. In: Long, G., Yu, X., Wang, S. (eds) AI 2021: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13151. Springer, Cham. https://doi.org/10.1007/978-3-030-97546-3_39

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  • DOI: https://doi.org/10.1007/978-3-030-97546-3_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97545-6

  • Online ISBN: 978-3-030-97546-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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