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The Chinese Journal of Artificial Intelligence

Editor-in-Chief

ISSN (Print): 2666-7827
ISSN (Online): 2666-7835

Research Article

Parameter Sensitivity Analysis of the Democratic Behavior of Swarm Robots

Author(s): Andreas Hugli, Marco A. G. Pereira, Rolf Dornberger and Thomas Hanne*

Volume 1, Issue 2, 2022

Published on: 06 September, 2022

Article ID: e090322201940 Pages: 13

DOI: 10.2174/2666782701666220309104945

Price: $65

Abstract

Aims: The purpose of this study is to determine under what conditions, such as noise and malfunction, successful consensus achievement in swarm robotics is possible.

Background: Swarm robots can be used to solve exploration problems, such as the best-of-n problem. Consensus achievement plays a crucial role as the swarm must collectively agree on a solution. This task can be even more challenging considering noise and malfunctioning or rogue agents.

Objective: This study aims to determine how robust the consensus achievement algorithm is against noise and rogue agents, considering the effect of adding memory to the agents and further parameter tuning.

Methods: We implement a baseline based on the democratic honeybees algorithm and investigate the performance and robustness of the consensus achievement during a number of computational experiments. In particular, the number of agents in the swarm, the number of iterations, the number of positions an agent can visit per iteration, the number of neighbors an agent shares its best option with, and the majority threshold defining the majority based on a fraction of agents in the swarm, and the minimum number of iterations to achieve consensus are investigated regarding their impact.

Results: For better performance, memory has been implemented so that each agent remembers and retains their previous highest quality score if no one better has been found in the current exploration phase. We show that the algorithm is viable and offers robustness in the considered scenarios when memory is added. In particular, we establish a baseline for the democratic honeybees algorithm and ascertain adequate parameter values to ensure the algorithm's best performance. The algorithm is sufficiently robust against noise, and to an extent, against rogue agents. Furthermore, parameter tuning also proved to help the swarm explore very large search spaces.

Conclusion: The consensus algorithm appears sufficiently effective under adverse conditions such as noise and rogue agents, especially when countermeasures are considered.

Other: Further scenarios such as specific communication topologies could be investigated in future research.

Keywords: Index Terms, best-of-n, swarm robotics, democratic honeybees, consensus achievement, collective decision making.

[1]
Fister, I. Jr. Yang, X-S.; Fister, I.; Brest, J.; Fister, D. A brief review of nature-inspired algorithms for optimization. El-ektrotehniski Vestnik, 2013, 80(3), 116-122.
[2]
Nicolis, S.C.; Dussutour, A. Self-organization, collective decision making and resource exploitation strategies in social insects. Eur. Phys. J. B, 2008, 65(3), 379-385.
[http://dx.doi.org/10.1140/epjb/e2008-00334-3]
[3]
Valentini, G.; Ferrante, E.; Dorigo, M. The best-of-n problem in robot swarms: Formalization, state of the art, and novel perspec-tives. Front. Robot. AI, 2017, 4, 9.
[http://dx.doi.org/10.3389/frobt.2017.00009]
[4]
Pochon, Y.; Dornberger, R.; Zhong, V.J.; Korkut, S. Investigating the Democracy Behavior of Swarm Robots in the Case of a Best-of-n Selection In: IEEE Symposium Series on Computational In-telligence (SSCI), Bangalore, India November;2018 , pp. 743-748.
[http://dx.doi.org/10.1109/SSCI.2018.8628646]
[5]
Seeley, T.D. Honeybee democracy.In: Honeybee Democracy; Princeton University Press: Princeton, 2010.
[6]
Strobel, V.; Castelló Ferrer, E.; Dorigo, M. Managing byzantine robots via blockchain technologyin a swarm robotics collective decision making scenario. In: AAMAS ’18: Proceedings of the 17th International Conference on Autonomous Agents and Multi-Agent Systems, Stockholm, SwedenJuly 10-15, 2018, pp. 541-549.
[7]
Strobel, V.; Dorigo, M. Blockchain technology for robot swarms: A shared knowledge and reputation management system for col-lective estimation. IRIDIA - Technical Report Series, Technical Report No. TR/IRIDIA/2018-009, May;2018 , pp. 1-12.
[8]
Ebert, J.T.; Gauci, M.; Nagpal, R. Multi-feature collective decision making in robot swarms. AAMAS ’18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, Stockholm, SwedenJuly 10-15, 2018, pp. 1711-1719.
[9]
Petrenko, V.I.; Tebueva, F.B.; Ryabtsev, S.S.; Struchkov, I.V. Consensus achievement method for a robotic swarm about the most fre-quently feature of an environment. OP Conf. Ser.: Mater. Sci. Eng., 2020, 919, p. 042025.
[http://dx.doi.org/10.1088/1757-899X/919/4/042025]
[10]
Valentini, G. How robots in a large group make decisions as a whole? From biological inspiration to the design of distributed al-gorithms arXiv:1910.11262v2, 2019.
[11]
Maître, G.; Tuci, E.; Ferrante, E. Opinion dissemination in a swarm of simulated robots with stubborn agents: A comparative study. In: 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1-6.
[http://dx.doi.org/10.1109/CEC48606.2020.9185581]
[12]
Ramsey, M.T.; Bencsik, M.; Newton, M.I.; Reyes, M.; Pioz, M.; Crauser, D.; Delso, N.S.; Le Conte, Y. The prediction of swarming in honeybee colonies using vibrational spectra. Sci. Rep., 2020, 10(1), 9798.
[http://dx.doi.org/10.1038/s41598-020-66115-5] [PMID: 32546693]
[13]
Ebert, J.T.; Gauci, M.; Mallmann-Trenn, F.; Nagpa, R. Bayes Bots: Collective Bayesian decision-making in decentralized robot swarms. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France2020, pp. 7186-7192.
[http://dx.doi.org/10.1109/ICRA40945.2020.9196584]
[14]
Aysal, T.C.; Barner, K.E. Convergence of consensus models with stochastic disturbances. IEEE Trans. Inf. Theory, 2010, 56(8), 4101-4113.
[http://dx.doi.org/10.1109/TIT.2010.2050940]
[15]
Hansen, N.; Finck, S.; Ros, R.; Auger, A. Real-parameter blackbox optimization benchmarking 2009: Noiseless functions definitions. Ph.D Thesis, INRIA, France, 2009.
[16]
Liu, Q.; van Wyk, B.J.; Du, S.; Sun, Y. Dynamic small world network topology for particle swarm optimization. Int. J. Pattern Recognit. Artif. Intell., 2016, 30(09), 1660009.
[http://dx.doi.org/10.1142/S0218001416600090]
[17]
Rodriguez, M.A.; Steinbock, D.J.; Watkins, J.H.; Gershenson, C.; Bollen, J.; Grey, V.; deGraf, B. Smartocracy: Social networks for collective decision making. In: 40th Annual Hawaii International Conference on System Sciences (HICSS’07), Waikoloa, HI, Hawaii 2007, 90

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