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Each agent is modeled as a probabilistic finite state machine with a preference for a finite number of options defined as a probability distribution. The most preferred option, called <jats:italic>exhibited decision<\/jats:italic>, determines the agent\u2019s state. The state transition is governed by internally updating this preference based on the states of neighboring agents and their entropy-based <jats:italic>levels of certainty<\/jats:italic>. Swarm agents continuously update their preferences by exchanging the exhibited decisions and the certainty values among the locally connected neighbors, leading to consensus towards an agreed-upon decision. The presented method is evaluated for its scalability over the swarm size and the number of options and its reliability under different conditions. Adopting classical best-of-<jats:italic>N<\/jats:italic> target selection scenarios, the algorithm is compared with three existing methods, the majority rule, frequency-based method, and <jats:italic>k<\/jats:italic>-unanimity method. The evaluation results show that the entropy-based method is reliable and efficient in these consensus problems.<\/jats:p>","DOI":"10.1007\/s11721-023-00226-3","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T16:04:37Z","timestamp":1684166677000},"page":"283-303","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Consensus decision-making in artificial swarms via entropy-based local negotiation and preference updating"],"prefix":"10.1007","volume":"17","author":[{"given":"Chuanqi","family":"Zheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiju","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,15]]},"reference":[{"key":"226_CR1","doi-asserted-by":"crossref","unstructured":"Amorim, T., Nascimento, T., Petracek, P., De Masi, G., Ferrante, E., & Saska, M. 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