Swarm collective wisdom: A fuzzy-based consensus approach for evaluating agents confidence in global states

Consensus achievement is a class of problems in which a group of agents, such as a swarm, needs to collectively reach a common decision to select one of the available options. Many consensus achievement strategies were proposed in which an agent forms its opinion and exchanges it with the other agents to reach a collective decision. To facilitate the decision making process, agents which are highly confident in their opinions are commonly given a higher chance to influence the collective's decision making. However, the use of subjective metrics for confidence could degrade the performance of the state-of-theart algorithms in complex scenarios where agents with wrong opinions can be the most confident. To tackle this problem, we propose an objective metric for confidence by using experience to learn the mapping between the information available to an agent and the probability that the agent's opinion is correct. To compute its confidence level, an agent feeds data from its local observations, as well as the received neighbours' opinions, into a fuzzy inference system (FIS) that uses these inputs to estimate confidence. The proposed strategy is distributed and it requires the agents to communicate locally using messages containing only their ID and opinions. Our strategy is evaluated under scenarios with different levels of complexity. The results show that our algorithm outperforms the state-of-the-art algorithms in terms of its accuracy, task time, and ability to reach majority. The proposed approach was also shown to maintain its success, even in the most complex environments.