A bio-inspired spatial defence strategy for collective decision making in self-organized swarms

Conference paper


Prasetyo, J., De Masi, G., Zakir, R., Alkilabi, M., Tuci, E. and Ferrante, E. 2021. A bio-inspired spatial defence strategy for collective decision making in self-organized swarms. GECCO 2021. Lille, France 10 - 14 Jul 2021 ACM. pp. 49-56 https://doi.org/10.1145/3449639.3459356
TypeConference paper
TitleA bio-inspired spatial defence strategy for collective decision making in self-organized swarms
AuthorsPrasetyo, J., De Masi, G., Zakir, R., Alkilabi, M., Tuci, E. and Ferrante, E.
Abstract

In collective decision-making, individuals in a swarm reach consensus on a decision using only local interactions without any centralized control. In the context of the best-of-n problem - characterized by n discrete alternatives - it has been shown that consensus to the best option can be reached if individuals disseminate that option more than the other options. Besides being used as a mechanism to modulate positive feedback, long dissemination times could potentially also be used in an adversarial way, whereby adversarial swarms could infiltrate the system and propagate bad decisions using aggressive dissemination strategies. Motivated by the above scenario, in this paper we propose a bio-inspired defence strategy that allows the swarm to be resilient against options that can be disseminated for longer times. This strategy mainly consists in reducing the mobility of the agents that are associated to options disseminated for a shorter amount of time, allowing the swarm to converge to this option. We study the effectiveness of this strategy using two classical decision mechanisms, the voter model and the majority rule, showing that the majority rule is necessary in our setting for this strategy to work. The strategy has also been validated on a real Kilobots experiment.

ConferenceGECCO 2021
Page range49-56
Proceedings TitleGECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference
ISBN
Hardcover9781450383509
PublisherACM
Publication dates
Print26 Jun 2021
Publication process dates
Deposited09 Jul 2021
Accepted26 Mar 2021
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1145/3449639.3459356
LanguageEnglish
Book titleGECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference
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