Consensus decision-making in artificial swarms via entropy-based local negotiation and preference updating
Chuanqi Zheng, Kiju Lee
Abstract
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.