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We study how robot swarms can achieve a consensus on the best among a set of n possible options available in the environment. While the robots rely on local communication with one another, follow simple rules, and make estimates of the option's qualities subject to measurement errors, the swarm as a whole is able to make accurate collective decisions. We compare the performance of two prominent decision-making algorithms that are based, respectively, on the directswitching and the cross-inhibition models, both of which are well-suited for simplistic robots. Most studies used these models to let robots achieve consensus by solely relying on social interactions and ignored the aspect of enabling robots to self-source information from the environment. However, in order to select the best option, we deem sampling environmental information crucial for the successful performance of the task. Through robot-swarm simulations, we show that swarms programmed with the direct-switching model are only able to make consensus decisions in asymmetric environments where options have different quality values. Instead, using cross-inhibition, the robot swarm can also break decision deadlocks and reach a consensus in symmetric environments with equal quality options. We investigate the mechanistic causes of such differences and we find that the time the robots spend in a state of indecision is a key parameter to break the symmetry. This research highlights the importance of considering both social and environmental information when studying collective decision-making.
In this paper we study a generalized case of best-of-n model, which considers three kind of agents: zealots, individuals who remain stubborn and do not change their opinion; informed agents, individuals that can change their opinion, are able to assess the quality of the different options; and uninformed agents, individuals that can change their opinion but are not able to assess the quality of the different opinions. We study the consensus in different regimes: we vary the quality of the options, the percentage of zealots and the percentage of informed versus uninformed agents. We also consider two decision mechanisms: the voter and majority rule. We study this problem using numerical simulations and mathematical models, and we validate our findings on physical kilobot experiments. We find that (1) if the number of zealots for the lowest quality option is not too high, the decision-making process is driven toward the highest quality option; (2) this effect can be improved increasing the number of informed agents that can counteract the effect of adverse zealots; (3) when the two options have very similar qualities, in order to keep high consensus to the best quality it is necessary to have higher proportions of informed agents.
General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
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