Consensus decision-making in artificial swarms via entropy-based local negotiation and preference updating

【Author】 Zheng, Chuanqi; Lee, Kiju

【Source】SWARM INTELLIGENCE

【影响因子】3.727

【Abstract】This paper presents an entropy-based consensus algorithm for a swarm of artificial agents with limited sensing, communication, and processing capabilities. 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 exhibited decision, determines the agent's state. The state transition is governed by internally updating this preference based on the states of neighboring agents and their entropy-based levels of certainty. 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-N target selection scenarios, the algorithm is compared with three existing methods, the majority rule, frequency-based method, and k-unanimity method. The evaluation results show that the entropy-based method is reliable and efficient in these consensus problems.

【Keywords】Consensus algorithm; Swarm robotics; Distributed decision-making

【发表时间】2023 2023 MAY 15

【收录时间】2023-06-05

【文献类型】理论模型

【主题类别】

区块链技术-核心技术-共识机制