Distributed Process Monitoring for Multiagent Systems Through Cognitive Learning

Multiagent systems are usually large-scaled with a growing degree of intelligence and integration. Direct applications of traditional (centralized) methods will become incompetent for effective process monitoring of multiagent systems. It necessitates the cognitive learning strategies that determine the effective interactions among subsystems or individuals. Therefore, in order to improve the monitoring performance, this article targets the development of a new distributed process monitoring method that has the cognitive learning ability by embedding an adaptive pickup rule. The proposed cognitive learning-based method can reduce the computation loads in both offline and online phases because only necessary information exchange (or communication topology) is involved. Furthermore, the threshold used for system monitoring is obtained by developing a fast search algorithm based on the statistical learning theory. Case studies on the wastewater treatment system, which can be regarded as a typical multiagent system, demonstrate the superiority of the proposed distributed process-monitoring method.